Strategies for EELS Data Analysis. Introducing UMAP and HDBSCAN for Dimensionality Reduction and Clustering
Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electro...
Uloženo v:
| Vydáno v: | Microscopy and microanalysis Ročník 28; číslo 1; s. 109 - 122 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York, USA
Cambridge University Press
01.02.2022
Oxford University Press |
| Témata: | |
| ISSN: | 1431-9276, 1435-8115, 1435-8115 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied. |
|---|---|
| AbstractList | Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied. Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied.Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied. |
| Author | Estradé, Sònia Blanco-Portals, Javier Peiró, Francesca |
| Author_xml | – sequence: 1 givenname: Javier orcidid: 0000-0002-7037-269X surname: Blanco-Portals fullname: Blanco-Portals, Javier email: jblanco@ub.edu organization: 1LENS-MIND, Department of Electronics and Biomedical Engineering, Universitat de Barcelona, 08028 Barcelona, Spain – sequence: 2 givenname: Francesca surname: Peiró fullname: Peiró, Francesca organization: 1LENS-MIND, Department of Electronics and Biomedical Engineering, Universitat de Barcelona, 08028 Barcelona, Spain – sequence: 3 givenname: Sònia surname: Estradé fullname: Estradé, Sònia organization: 1LENS-MIND, Department of Electronics and Biomedical Engineering, Universitat de Barcelona, 08028 Barcelona, Spain |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35177136$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUtvGyEQgFGVqnm0P6CXCimXXjYZwMDu0bGdh-Q-VDfnFbvMWqT7SIE9-N8HO44iJUpOzMD3jYaZY3LQDz0S8pXBGQOmz1dsIljBteIpFapQH8hRupJZzpg82MUs274fkuMQ7gBAgFafyKGQTOtkHJF_q-hNxLXDQJvB08ViuaJzEw2d9qbdBBfO6E0f_WDH2vVrevtj-pua3tLr-cVqNv25k-auwz64IRkubugfTHBM6Q6ctWOI6JP8mXxsTBvwy_48IbeXi7-z62z56-pmNl1mtdAiZpUQopa5bQANV41gFeSgZAUNgK1RamU1CoOYAmByAjZnHLmyBU5yXilxQr4_1r33w_8RQyw7F2psW9PjMIaSKwEFB80hoacv0Lth9OkfW4oXsiiE5In6tqfGqkNb3nvXGb8pn8aYAP0I1H4IwWNT1i6a7QjSdF1bMii3CytfLSyZ7IX5VPw9R-wd01Xe2TU-d_229QDfO6N7 |
| CitedBy_id | crossref_primary_10_1088_2632_2153_add8e1 crossref_primary_10_1093_pnasnexus_pgae197 crossref_primary_10_1016_j_ecoinf_2025_103058 crossref_primary_10_1016_j_ecoinf_2025_103222 crossref_primary_10_3390_computation13060144 crossref_primary_10_1016_j_micron_2025_103858 crossref_primary_10_1016_j_ultramic_2023_113828 crossref_primary_10_1126_sciadv_adr8793 crossref_primary_10_1016_j_dsim_2025_05_001 crossref_primary_10_1007_s10639_023_12010_1 crossref_primary_10_1016_j_iot_2025_101764 crossref_primary_10_1007_s42001_024_00273_8 crossref_primary_10_1016_j_sab_2025_107137 crossref_primary_10_1051_bioconf_202412910015 crossref_primary_10_3390_su151813577 crossref_primary_10_1016_j_ecoinf_2023_102089 crossref_primary_10_1093_mam_ozae014 crossref_primary_10_1002_adsu_202300559 |
| Cites_doi | 10.1007/b107408 10.1016/j.laa.2005.06.025 10.1016/j.ultramic.2021.113403 10.1002/jemt.22099 10.1016/j.ultramic.2012.10.001 10.1016/j.ultramic.2010.10.001 10.1017/S1431927615015664 10.1587/transfun.E92.A.708 10.1016/j.patrec.2009.09.011 10.1145/304181.304187 10.1016/j.ultramic.2021.113314 10.1017/S1431927620019856 10.1016/j.cosrev.2021.100378 10.1016/j.ultramic.2017.06.023 10.1016/j.micron.2011.07.008 10.1080/01621459.1963.10500845 10.1016/B978-044452701-1.00067-3 10.1126/science.aao0865 10.1561/2200000055 10.1017/S1431927620020486 10.1109/34.868688 10.1109/I-SMAC49090.2020.9243502 10.21105/joss.00205 10.1017/S1431927612002243 10.1016/j.ultramic.2017.11.010 10.1002/adom.202001808 10.1162/NECO_a_00168 10.1109/TPAMI.1979.4766926 10.1103/PhysRevLett.99.086102 10.1145/3068335 10.1080/01621459.1983.10478008 10.1007/978-3-319-13212-9 10.1016/j.jsb.2021.107745 10.1126/science.290.5500.2319 10.1038/ncomms3960 10.1007/s00357-014-9161-z 10.1155/2018/8019232 10.1016/j.ultramic.2016.08.006 10.1021/acs.nanolett.6b01922 10.1002/0470013192.bsa501 10.1016/j.ultramic.2012.07.020 10.1016/j.jeurceramsoc.2014.02.017 10.1021/acs.jpcc.7b01749 10.1017/S1431927614000440 10.1016/0304-3991(90)90070-3 10.1109/TIT.1982.1056489 10.1038/nbt.4314 10.1016/j.ultramic.2016.10.008 10.1145/2733381 10.1145/502807.502808 10.1109/TKDE.2012.51 10.1016/j.micron.2020.102981 10.1088/1367-2630/ab7a89 10.1038/s41598-017-07709-4 |
| ContentType | Journal Article |
| Copyright | Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America |
| Copyright_xml | – notice: Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America |
| DBID | AAYXX CITATION NPM 3V. 7QO 7RV 7TK 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB0 LK8 M0S M1P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 |
| DOI | 10.1017/S1431927621013696 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Nursing & Allied Health Database Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic ProQuest Central Student CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1435-8115 |
| EndPage | 122 |
| ExternalDocumentID | 35177136 10_1017_S1431927621013696 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Agència de Gestió d'Ajuts Universitaris i de Recerca Spanish Research Network |
| GroupedDBID | --- -E. .FH 09C 0E1 0R~ 123 29M 3V. 4.4 53G 5VS 5WD 74X 74Y 7RV 7X7 7~V 88E 8FE 8FG 8FH 8FI 8FJ 8R4 8R5 9M5 AAAZR AABES AABWE AACJH AAEED AAGFV AAKTX AARAB AASVR AATID AAUKB ABBXD ABEFU ABITZ ABJNI ABKKG ABMWE ABMYL ABPTD ABQTM ABQWD ABROB ABTCQ ABUWG ABWCF ABWST ABZCX ACBEK ACBMC ACCHT ACFRR ACGFS ACIMK ACIPB ACIWK ACPRK ACQFJ ACQPF ACREK ACUFI ACUIJ ACUYZ ACWGA ACYZP ACZBM ACZUX ACZWT ADAZD ADBBV ADCGK ADFEC ADGEJ ADIPN ADIYS ADKIL ADOCW ADOVH ADQBN ADRDM ADVEK AEBAK AEHGV AEMTW AENEX AENGE AEYYC AFFUJ AFGWE AFKQG AFKRA AFKSM AFLOS AFLVW AFRAH AFUTZ AGABE AGBYD AGJUD AGOOT AHIPN AHLTW AHMBA AHQXX AIGNW AIHIV AIOIP AISIE AJ7 AJCYY AJPFC AJQAS ALMA_UNASSIGNED_HOLDINGS ALVPG ALWZO ANFBD ANPSP AQJOH ARABE ARAPS ATUCA AUXHV AZGZS BBLKV BBNVY BENPR BGHMG BGLVJ BGNMA BHPHI BKEYQ BLZWO BMAJL BPHCQ BRIRG BVXVI C0O CAG CBIIA CCPQU CCQAD CFAFE CJCSC COF CS3 DC4 DOHLZ DU5 EBS EJD EX3 F5P FYUFA HCIFZ HG- HMCUK HST HZ~ I.6 IH6 IOEEP IS6 I~P J36 J38 J3A JHPGK JKPOH JQKCU JVRFK KCGVB KFECR KOP L98 LAS LK8 LW7 M-V M1P M4Y M7P NAPCQ NIKVX NU0 O9- OBOKY OJZSN OVD OWPYF OYBOY P62 PQQKQ PROAC PSQYO PYCCK Q2X RAMDC RCA RIG RNS ROL RR0 S6- S6U SAAAG SDH SY4 T9M TEORI UKHRP UT1 UU6 VUG WFFJZ WOW WQ3 WXU WXY WYP ZYDXJ AAPXW AAUAY AAYXX ABDFA ABDTM ABEJV ABGNP ABMNT ABPQP ABVGC ABVKB ABXVV ABZEO ACVCV ACZBC ADNBA ADVOB ADYJX AEMTJ AEUYN AFFHD AGMDO AHGBF AJBYB AJDVS AJNCP ATGXG BCRHZ CITATION H13 NU- PHGZM PHGZT PJZUB PPXIY PQGLB ROX NPM 7QO 7TK 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 PUEGO |
| ID | FETCH-LOGICAL-c373t-b333c58df0ea26f31b08065b0f00dce576d7e3aee76d01540d812e26d9e482b63 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 23 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000757458900011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1431-9276 1435-8115 |
| IngestDate | Thu Oct 02 05:12:42 EDT 2025 Mon Oct 06 18:25:45 EDT 2025 Thu Apr 03 07:08:42 EDT 2025 Tue Nov 18 21:54:55 EST 2025 Sat Nov 29 05:44:15 EST 2025 Wed Mar 13 05:54:07 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | HDBSCAN UMAP dimensionality reduction clustering EELS |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c373t-b333c58df0ea26f31b08065b0f00dce576d7e3aee76d01540d812e26d9e482b63 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7037-269X |
| PMID | 35177136 |
| PQID | 2629599352 |
| PQPubID | 33692 |
| PageCount | 14 |
| ParticipantIDs | proquest_miscellaneous_2630920720 proquest_journals_2629599352 pubmed_primary_35177136 crossref_citationtrail_10_1017_S1431927621013696 crossref_primary_10_1017_S1431927621013696 cambridge_journals_10_1017_S1431927621013696 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-02-01 |
| PublicationDateYYYYMMDD | 2022-02-01 |
| PublicationDate_xml | – month: 02 year: 2022 text: 2022-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York, USA |
| PublicationPlace_xml | – name: New York, USA – name: United States – name: Oxford |
| PublicationTitle | Microscopy and microanalysis |
| PublicationTitleAlternate | Microsc Microanal |
| PublicationYear | 2022 |
| Publisher | Cambridge University Press Oxford University Press |
| Publisher_xml | – name: Cambridge University Press – name: Oxford University Press |
| References | 2017; 7 2017; 42 2013; 4 2017; 2 2006; 416 2013; 25 2012; 122 2020; 20 2013; 125 2012; 18 2011; 111 2000; 290 2014; 20 1982; 28 2001 2014; 15 1979; 3 2007; 4 2011; 23 2021; 232 2017; 121 2021; 40 2021; 9 2018; 185 2010; 31 1990; 34 1999; 28 2000; 22 2019; 37 2015; 10 2009 2021; 140 2017; 172 2007; 99 2016; 16 2012; 75 1983; 78 1963; 58 2018; 2018 2018; 359 2021 1967; 1 2021; 213 2020; 26 2017; 182 2016; 374 2020; 22 2001; 33 2009; 2 2016; 170 2012; 43 2014; 34 2016; 9 2014; 31 2016; 22 S1431927621013696_ref32 S1431927621013696_ref31 S1431927621013696_ref30 S1431927621013696_ref25 S1431927621013696_ref23 S1431927621013696_ref29 S1431927621013696_ref28 S1431927621013696_ref26 S1431927621013696_ref61 S1431927621013696_ref60 Shi (S1431927621013696_ref48) 2000; 22 S1431927621013696_ref21 S1431927621013696_ref65 S1431927621013696_ref64 S1431927621013696_ref20 S1431927621013696_ref63 S1431927621013696_ref62 S1431927621013696_ref14 S1431927621013696_ref58 S1431927621013696_ref57 S1431927621013696_ref13 S1431927621013696_ref56 S1431927621013696_ref12 S1431927621013696_ref11 S1431927621013696_ref55 S1431927621013696_ref18 S1431927621013696_ref17 S1431927621013696_ref16 S1431927621013696_ref15 S1431927621013696_ref59 S1431927621013696_ref19 Maaten Van Der (S1431927621013696_ref34) 2014; 15 S1431927621013696_ref8 S1431927621013696_ref9 S1431927621013696_ref50 S1431927621013696_ref6 S1431927621013696_ref7 S1431927621013696_ref4 S1431927621013696_ref5 S1431927621013696_ref10 S1431927621013696_ref2 S1431927621013696_ref54 S1431927621013696_ref53 S1431927621013696_ref3 S1431927621013696_ref52 S1431927621013696_ref1 Han (S1431927621013696_ref22) 2011 S1431927621013696_ref47 S1431927621013696_ref46 Hershey (S1431927621013696_ref24) 2007; 4 S1431927621013696_ref45 S1431927621013696_ref44 S1431927621013696_ref49 MacQueen (S1431927621013696_ref35) 1967; 1 Ng (S1431927621013696_ref40) 2001 S1431927621013696_ref43 S1431927621013696_ref42 S1431927621013696_ref41 S1431927621013696_ref36 Jolliffe (S1431927621013696_ref27) 2016; 374 S1431927621013696_ref33 S1431927621013696_ref39 S1431927621013696_ref38 S1431927621013696_ref37 Spurgeon (S1431927621013696_ref51) 2020; 20 |
| References_xml | – volume: 121 start-page: 10552 year: 2017 end-page: 10561 article-title: Quantitative analysis of electron beam damage in organic thin films publication-title: J Phys Chem C – volume: 213 start-page: 107745 year: 2021 article-title: Practical considerations for using K3 cameras in CDS mode for high-resolution and high-throughput single particle cryo-EM publication-title: J Struct Biol – volume: 22 start-page: 033029 year: 2020 article-title: Novel spectral unmixing approach for electron energy-loss spectroscopy publication-title: New J Phys – volume: 34 start-page: 3007 year: 2014 end-page: 3018 article-title: Electron-beam damage and point defects near grain boundaries in cerium oxide publication-title: J Eur Ceram Soc – start-page: 849 year: 2001 end-page: 856 publication-title: Advances in Neural Information Processing Systems – volume: 33 start-page: 273 year: 2001 end-page: 321 article-title: Searching in metric spaces publication-title: ACM Comput Surv – volume: 1 start-page: 281 year: 1967 end-page: 297 article-title: Some methods for classification and analysis of multivariate observations publication-title: In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability – volume: 22 start-page: 237 year: 2016 end-page: 249 article-title: High dynamic range pixel array detector for scanning transmission electron microscopy publication-title: Microsc Microanal – volume: 232 start-page: 113403 year: 2021 article-title: WhatEELS. A new python-based interactive software solution for ELNES analysis combining clustering and NLLS publication-title: Ultramicroscopy – volume: 20 start-page: 698 year: 2014 end-page: 705 article-title: Oxide wizard: An EELS application to characterize the white lines of transition metal edges publication-title: Microsc Microanal – volume: 374 year: 2016 article-title: Principal component analysis: A review and recent developments publication-title: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences – volume: 37 start-page: 38 year: 2019 end-page: 47 article-title: Dimensionality reduction for visualizing single-cell data using UMAP publication-title: Nat Biotechnol – volume: 111 start-page: 169 year: 2011 end-page: 176 article-title: Mapping titanium and tin oxide phases using EELS: An application of independent component analysis publication-title: Ultramicroscopy – volume: 185 start-page: 42 year: 2018 end-page: 48 article-title: Clustering analysis strategies for electron energy loss spectroscopy (EELS) publication-title: Ultramicroscopy – volume: 18 start-page: 78 year: 2012 end-page: 79 article-title: K2: A super-resolution electron counting direct detection camera for cryo-EM publication-title: Microsc Microanal – volume: 2018 start-page: 1 year: 2018 end-page: 17 article-title: On the use of t-distributed stochastic neighbor embedding for data visualization and classification of individuals with Parkinson's disease publication-title: Computational and Mathematical Methods in Medicine – volume: 42 year: 2017 article-title: DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN publication-title: ACM Transactions on Database Systems – volume: 172 start-page: 40 year: 2017 end-page: 46 article-title: Can we use PCA to detect small signals in noisy data? publication-title: Ultramicroscopy – volume: 4 start-page: IV year: 2007 end-page: 317 article-title: Approximating the Kullback Leibler divergence between Gaussian mixture models publication-title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings – volume: 20 start-page: 274 year: 2020 end-page: 279 article-title: Towards data-driven next-generation transmission electron microscopy publication-title: Nature Materials 2020 20:3 – volume: 359 start-page: 675 year: 2018 end-page: 679 article-title: Atomic-resolution transmission electron microscopy of electron beam–sensitive crystalline materials publication-title: Science – volume: 2 start-page: 205 year: 2017 article-title: Hdbscan: Hierarchical density based clustering publication-title: The Journal of Open Source Software – volume: 4 start-page: 1 year: 2013 end-page: 8 article-title: Robust antiferromagnetic coupling in hard-soft bi-magnetic core/shell nanoparticles publication-title: Nat Commun – volume: 43 start-page: 57 year: 2012 end-page: 67 article-title: Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images publication-title: Micron – volume: 40 start-page: 100378 year: 2021 article-title: Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) publication-title: Comput Sci Rev – volume: 182 start-page: 191 year: 2017 end-page: 194 article-title: On the loss of information in PCA of spectrum-images publication-title: Ultramicroscopy – volume: 15 start-page: 3221 year: 2014 end-page: 3245 article-title: Accelerating t-SNE using tree-based algorithms publication-title: Journal of Machine Learning Research – volume: 290 start-page: 2319 year: 2000 end-page: 2323 article-title: A global geometric framework for nonlinear dimensionality reduction publication-title: Science – volume: 9 start-page: 1 year: 2016 end-page: 118 article-title: Generalized low rank models publication-title: Found Trends Mach Learn – volume: 2 start-page: 635 year: 2009 end-page: 654 article-title: Density-based clustering methods publication-title: Compr Chemom – volume: 26 start-page: 1928 year: 2020 end-page: 1930 article-title: Hybrid pixel EELS detector: Low noise, high speed, and large dynamic range publication-title: Microsc Microanal – volume: 3 start-page: 306 year: 1979 end-page: 307 article-title: A problem of dimensionality: A simple example publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 31 start-page: 651 year: 2010 end-page: 666 article-title: Data clustering: 50 years beyond K-means publication-title: Pattern Recog Lett – volume: 7 start-page: 1 year: 2017 end-page: 14 article-title: Direct detection electron energy-loss spectroscopy: A method to push the limits of resolution and sensitivity publication-title: Sci Rep – volume: 22 start-page: 888 year: 2000 end-page: 905 article-title: Normalized cuts and image segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 16 start-page: 5068 year: 2016 end-page: 5073 article-title: 3D visualization of the iron oxidation state in FeO/Fe 3 O 4 core–shell nanocubes from electron energy loss tomography publication-title: Nano Lett – start-page: 113314 year: 2021 article-title: Dimensionality reduction and unsupervised clustering for EELS-SI publication-title: Ultramicroscopy – volume: 31 start-page: 274 year: 2014 end-page: 295 article-title: Ward's hierarchical agglomerative clustering method: Which algorithms implement ward's criterion? publication-title: Journal of Classification – volume: 28 start-page: 49 year: 1999 end-page: 60 article-title: OPTICS publication-title: ACM SIGMOD Record – volume: 78 start-page: 553 year: 1983 end-page: 569 article-title: A method for comparing two hierarchical clusterings publication-title: J Am Stat Assoc – volume: 170 start-page: 43 year: 2016 end-page: 59 article-title: Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization publication-title: Ultramicroscopy – volume: 25 start-page: 1336 year: 2013 end-page: 1353 article-title: Nonnegative matrix factorization: A comprehensive review publication-title: IEEE Trans Knowled Data Eng – volume: 125 start-page: 35 year: 2013 end-page: 42 article-title: Statistical consequences of applying a PCA noise filter on EELS spectrum images publication-title: Ultramicroscopy – volume: 416 start-page: 29 year: 2006 end-page: 47 article-title: Nonnegative matrix factorization for spectral data analysis publication-title: Linear Algebra and its Applications – volume: 23 start-page: 2421 year: 2011 end-page: 2456 article-title: Algorithms for nonnegative matrix factorization with the β-divergence publication-title: Neural Comput – volume: 75 start-page: 1550 year: 2012 end-page: 1556 article-title: Mechanisms of radiation damage in beam-sensitive specimens, for TEM accelerating voltages between 10 and 300 kV publication-title: Microsc Res Tech – volume: 99 start-page: 1 year: 2007 end-page: 4 article-title: Two-dimensional mapping of chemical information at atomic resolution publication-title: Phys Rev Lett – volume: 26 start-page: 2112 year: 2020 end-page: 2114 article-title: Development of clustering algorithm applied for the EELS analysis of advanced devices publication-title: Microsc Microanal – volume: 140 start-page: 102981 year: 2021 article-title: Benefits of direct electron detection and PCA for EELS investigation of organic photovoltaics materials publication-title: Micron – volume: 58 start-page: 236 year: 1963 end-page: 244 article-title: Hierarchical grouping to optimize an objective function publication-title: J Am Stat Assoc – volume: 34 start-page: 165 year: 1990 end-page: 178 article-title: EELS elemental mapping with unconventional methods I. Theoretical basis: Image analysis with multivariate statistics and entropy concepts publication-title: Ultramicroscopy – start-page: 708 year: 2009 end-page: 721 article-title: Fast local algorithms for large scale nonnegative matrix and tensor factorizations publication-title: IEICE Transactions on Fundamentals of Electronics, Commun Comput Sci – volume: 122 start-page: 12 year: 2012 end-page: 18 article-title: EEL spectroscopic tomography: Towards a new dimension in nanomaterials analysis publication-title: Ultramicroscopy – volume: 28 start-page: 129 year: 1982 end-page: 137 article-title: Least squares quantization in PCM publication-title: IEEE Transactions on Information Theory – volume: 10 start-page: 1 year: 2015 end-page: 51 article-title: Hierarchical density estimates for data clustering, visualization, and outlier detection publication-title: ACM Trans Knowl Discov From Data – volume: 9 start-page: 1 year: 2021 end-page: 13 article-title: Separating physically distinct mechanisms in complex infrared plasmonic nanostructures via machine learning enhanced electron energy loss spectroscopy publication-title: Adv Opt Mater – ident: S1431927621013696_ref36 doi: 10.1007/b107408 – ident: S1431927621013696_ref43 doi: 10.1016/j.laa.2005.06.025 – ident: S1431927621013696_ref5 doi: 10.1016/j.ultramic.2021.113403 – ident: S1431927621013696_ref16 doi: 10.1002/jemt.22099 – ident: S1431927621013696_ref31 doi: 10.1016/j.ultramic.2012.10.001 – ident: S1431927621013696_ref14 doi: 10.1016/j.ultramic.2010.10.001 – ident: S1431927621013696_ref38 – ident: S1431927621013696_ref53 doi: 10.1017/S1431927615015664 – ident: S1431927621013696_ref12 doi: 10.1587/transfun.E92.A.708 – ident: S1431927621013696_ref25 doi: 10.1016/j.patrec.2009.09.011 – ident: S1431927621013696_ref2 doi: 10.1145/304181.304187 – ident: S1431927621013696_ref46 doi: 10.1016/j.ultramic.2021.113314 – ident: S1431927621013696_ref44 doi: 10.1017/S1431927620019856 – ident: S1431927621013696_ref29 – ident: S1431927621013696_ref3 doi: 10.1016/j.cosrev.2021.100378 – ident: S1431927621013696_ref45 doi: 10.1016/j.ultramic.2017.06.023 – ident: S1431927621013696_ref15 doi: 10.1016/j.micron.2011.07.008 – volume: 20 start-page: 274 year: 2020 ident: S1431927621013696_ref51 article-title: Towards data-driven next-generation transmission electron microscopy publication-title: Nature Materials 2020 20:3 – ident: S1431927621013696_ref61 doi: 10.1080/01621459.1963.10500845 – ident: S1431927621013696_ref32 – ident: S1431927621013696_ref13 doi: 10.1016/B978-044452701-1.00067-3 – ident: S1431927621013696_ref65 doi: 10.1126/science.aao0865 – ident: S1431927621013696_ref59 doi: 10.1561/2200000055 – ident: S1431927621013696_ref10 doi: 10.1017/S1431927620020486 – volume: 22 start-page: 888 year: 2000 ident: S1431927621013696_ref48 article-title: Normalized cuts and image segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.868688 – ident: S1431927621013696_ref42 doi: 10.1109/I-SMAC49090.2020.9243502 – ident: S1431927621013696_ref37 doi: 10.21105/joss.00205 – ident: S1431927621013696_ref6 doi: 10.1017/S1431927612002243 – volume: 374 year: 2016 ident: S1431927621013696_ref27 article-title: Principal component analysis: A review and recent developments publication-title: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences – ident: S1431927621013696_ref56 doi: 10.1016/j.ultramic.2017.11.010 – ident: S1431927621013696_ref28 doi: 10.1002/adom.202001808 – ident: S1431927621013696_ref18 doi: 10.1162/NECO_a_00168 – ident: S1431927621013696_ref58 doi: 10.1109/TPAMI.1979.4766926 – ident: S1431927621013696_ref7 doi: 10.1103/PhysRevLett.99.086102 – ident: S1431927621013696_ref47 doi: 10.1145/3068335 – ident: S1431927621013696_ref19 doi: 10.1080/01621459.1983.10478008 – ident: S1431927621013696_ref1 doi: 10.1007/978-3-319-13212-9 – ident: S1431927621013696_ref52 doi: 10.1016/j.jsb.2021.107745 – ident: S1431927621013696_ref54 doi: 10.1126/science.290.5500.2319 – ident: S1431927621013696_ref17 doi: 10.1038/ncomms3960 – ident: S1431927621013696_ref39 doi: 10.1007/s00357-014-9161-z – ident: S1431927621013696_ref41 doi: 10.1155/2018/8019232 – ident: S1431927621013696_ref49 doi: 10.1016/j.ultramic.2016.08.006 – ident: S1431927621013696_ref55 doi: 10.1021/acs.nanolett.6b01922 – ident: S1431927621013696_ref26 doi: 10.1002/0470013192.bsa501 – ident: S1431927621013696_ref63 doi: 10.1016/j.ultramic.2012.07.020 – volume: 15 start-page: 3221 year: 2014 ident: S1431927621013696_ref34 article-title: Accelerating t-SNE using tree-based algorithms publication-title: Journal of Machine Learning Research – ident: S1431927621013696_ref62 doi: 10.1016/j.jeurceramsoc.2014.02.017 – start-page: 849 year: 2001 ident: S1431927621013696_ref40 publication-title: Advances in Neural Information Processing Systems – volume: 1 start-page: 281 year: 1967 ident: S1431927621013696_ref35 article-title: Some methods for classification and analysis of multivariate observations publication-title: In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability – ident: S1431927621013696_ref30 doi: 10.1021/acs.jpcc.7b01749 – ident: S1431927621013696_ref64 doi: 10.1017/S1431927614000440 – volume-title: Data Mining: Concepts and Techniques. year: 2011 ident: S1431927621013696_ref22 – ident: S1431927621013696_ref57 doi: 10.1016/0304-3991(90)90070-3 – ident: S1431927621013696_ref33 doi: 10.1109/TIT.1982.1056489 – ident: S1431927621013696_ref4 doi: 10.1038/nbt.4314 – ident: S1431927621013696_ref50 doi: 10.1016/j.ultramic.2016.10.008 – volume: 4 start-page: IV year: 2007 ident: S1431927621013696_ref24 article-title: Approximating the Kullback Leibler divergence between Gaussian mixture models publication-title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings – ident: S1431927621013696_ref9 doi: 10.1145/2733381 – ident: S1431927621013696_ref11 doi: 10.1145/502807.502808 – ident: S1431927621013696_ref60 doi: 10.1109/TKDE.2012.51 – ident: S1431927621013696_ref21 doi: 10.1016/j.micron.2020.102981 – ident: S1431927621013696_ref8 doi: 10.1088/1367-2630/ab7a89 – ident: S1431927621013696_ref23 doi: 10.1038/s41598-017-07709-4 – ident: S1431927621013696_ref20 |
| SSID | ssj0003076 |
| Score | 2.4109528 |
| Snippet | Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new... |
| SourceID | proquest pubmed crossref cambridge |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 109 |
| SubjectTerms | Algorithms Classification Cluster analysis Clustering Core-shell particles Data analysis Datasets Electron energy loss spectroscopy Energy dissipation Energy loss Image segmentation Manganese Manganese oxides Nanoparticles Noise Software and Instrumentation Spectroscopy Spectrum analysis |
| Title | Strategies for EELS Data Analysis. Introducing UMAP and HDBSCAN for Dimensionality Reduction and Clustering |
| URI | https://www.cambridge.org/core/product/identifier/S1431927621013696/type/journal_article https://www.ncbi.nlm.nih.gov/pubmed/35177136 https://www.proquest.com/docview/2629599352 https://www.proquest.com/docview/2630920720 |
| Volume | 28 |
| WOSCitedRecordID | wos000757458900011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1435-8115 dateEnd: 20221231 omitProxy: false ssIdentifier: ssj0003076 issn: 1431-9276 databaseCode: M7P dateStart: 20020201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1435-8115 dateEnd: 20221231 omitProxy: false ssIdentifier: ssj0003076 issn: 1431-9276 databaseCode: 7X7 dateStart: 20020201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1435-8115 dateEnd: 20221231 omitProxy: false ssIdentifier: ssj0003076 issn: 1431-9276 databaseCode: 7RV dateStart: 20020201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1435-8115 dateEnd: 20221231 omitProxy: false ssIdentifier: ssj0003076 issn: 1431-9276 databaseCode: P5Z dateStart: 20020201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1435-8115 dateEnd: 20221231 omitProxy: false ssIdentifier: ssj0003076 issn: 1431-9276 databaseCode: BENPR dateStart: 20020201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dixMxEB_s1QNfPPVOrVdLBJ_EaDbpbnafpF_HCWdZWnsUX0q-Fo47tue1FfzvzezXKWJffAlLNpMEJpOZZCa_AXjLldKJtzOoVKZP-3FgqA6FpU7gOx8bBrIESbqQ02m8XCZpdeG2qcIq6z2x2Kjt2uAd-Uce8ST0yjTkn26_U8wahd7VKoVGC9qIksCL0L202Yn9-i1fF4mAJlxGtVcTIaOxEuv8kSfAnHa_Yyv8qaP-YXgWCujs6H-n_gQeV6YnGZRr5Sk8cPkzOCyTUf48husaqNZtiLdkyWRyMSdjtVWkBi75QD5jXLvdGa_vyOLLICUqt-R8PJyPBtOCaIzJAkqgD2_ekxkCwyLri4ajmx3CMnjiE1icTb6OzmmVioEaIcWWaiGECWObMad4lIlAM_TIapYxZo3zhxYrnVDO-Q-0ypj1hoPjkU1cP-Y6Es_hIF_n7iUQqUOWRUpxnVmvGcNYZipQQWL7mdBJoDvwvmHEqhKozaoMRpOrv_jWAVbzamUqWHPMrnGzj-RdQ3JbYnrsa9ytmXo_m3uOduBN89sLJnpbVO7WO2wjWMKZ5KwDL8qF04wmvAxI3_2r_Z2fwiOOry2KIPEuHGzvdu41PDQ_tlebux605OwSy6UsyrgH7eFkms56hRT4Mg2__QIwaASj |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VAoIL70egwCLBBbHtetf22geEQpIqUdMooq1UcTH7soSonNIkoP4pfiM73tgFIXLrgZtl76wfO6_1zHwD8IorpXPvZ1CpTEzjLDJUJ8JSJ7DOxyaRDCBJYzmZZMfH-XQDfja1MJhW2ejEWlHbmcF_5Ds85XnijWnC359-o9g1CqOrTQuNwBZ77vyH37LN3436fn1fc747OOwN6aqrADVCigXVQgiTZLZkTvG0FJFmGFzUrGTMGuf9byudUM75A3QwmPU20PHU5i7OuE6Fn_cKXI1jzlCKpsmnVvN7eQnVTCKiOZdpE0VFiGo8ief8FivCHnq_Yzn8aRP_4ejWBm_39v_2qe7ArZVrTbpBFu7ChqvuwfXQbPP8PnxtgHjdnHhPnQwG4wPSVwtFGmCWbTLCvH27NN6ek6P97pSoypJh_8NBrzupifrYDCEAmfjtC_mIwLfI2vXA3skSYSc88QM4upQ3fQib1axyj4FInbAyVYrr0nrLn2SyVJGKchuXQueR7sDbduGLlcKYFyHZThZ_8UkHWMMbhVnBtmP3kJN1JG9aktOAWbJu8FbDRBdPc8FBHXjZXvaKB6NJqnKzJY4RLOdMctaBR4FR27sJL-PST_9k_eQv4MbwcH9cjEeTvadwk2NlSZ0QvwWbi7OlewbXzPfFl_nZ81rOCHy-bG79Bf2YW8Q |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Strategies+for+EELS+Data+Analysis.+Introducing+UMAP+and+HDBSCAN+for+Dimensionality+Reduction+and+Clustering&rft.jtitle=Microscopy+and+microanalysis&rft.au=Blanco-Portals%2C+Javier&rft.au=Peir%C3%B3%2C+Francesca&rft.au=Estrad%C3%A9%2C+S%C3%B2nia&rft.date=2022-02-01&rft.pub=Oxford+University+Press&rft.issn=1431-9276&rft.eissn=1435-8115&rft.volume=28&rft.issue=1&rft.spage=109&rft.epage=122&rft_id=info:doi/10.1017%2FS1431927621013696 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1431-9276&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1431-9276&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1431-9276&client=summon |