Unsupervised feature selection using orthogonal encoder-decoder factorization
Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more effic...
Uložené v:
| Vydané v: | Information sciences Ročník 663; s. 120277 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.03.2024
|
| Predmet: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self-representation or pseudo-supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal Encoder-Decoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self-representation and pseudo-supervised approaches. This method draws inspiration from the self-representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo-supervised feature selection approach. To further enhance the part-based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency. |
|---|---|
| AbstractList | Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self-representation or pseudo-supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal Encoder-Decoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self-representation and pseudo-supervised approaches. This method draws inspiration from the self-representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo-supervised feature selection approach. To further enhance the part-based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency. |
| ArticleNumber | 120277 |
| Author | Pir Mohammadiani, Rojiar Seyedi, Seyed Amjad Mozafari, Maryam Akhlaghian Tab, Fardin |
| Author_xml | – sequence: 1 givenname: Maryam surname: Mozafari fullname: Mozafari, Maryam email: m.mozafari@uok.ac.ir – sequence: 2 givenname: Seyed Amjad orcidid: 0000-0003-2718-7146 surname: Seyedi fullname: Seyedi, Seyed Amjad email: amjadseyedi@uok.ac.ir – sequence: 3 givenname: Rojiar surname: Pir Mohammadiani fullname: Pir Mohammadiani, Rojiar email: r.pirmohamadiani@uok.ac.ir – sequence: 4 givenname: Fardin orcidid: 0000-0002-0300-9233 surname: Akhlaghian Tab fullname: Akhlaghian Tab, Fardin email: f.akhlaghian@uok.ac.ir |
| BookMark | eNp9kM9OwzAMhyM0JLbBA3DrC7Q4ado04oQm_klDXNg5SlN3ZCrJlHST4OnJNk4cdvHPB3-W_c3IxHmHhNxSKCjQ-m5TWBcLBowXNFUhLsiUNoLlNZN0QqYADHJgVXVFZjFuAICLup6St5WLuy2GvY3YZT3qcRcwizigGa132S5at858GD_92js9ZOiM7zDkHR4z67UZfbA_-jB-TS57PUS8-cs5WT09fixe8uX78-viYZkbJsWYSyOAc92AqXnT160UqRWV7kWDaYJrXbbIWiFYT6tSUy0BOskraFmlRSXLORGnvSb4GAP2ytjxeMEYtB0UBXWwojYqWVEHK-pkJZH0H7kN9kuH77PM_YnB9NLeYlDR2OQBOxuSJtV5e4b-BXQ5fdU |
| CitedBy_id | crossref_primary_10_1016_j_asoc_2025_113080 crossref_primary_10_1016_j_knosys_2024_112902 crossref_primary_10_1016_j_patcog_2025_112211 crossref_primary_10_1016_j_physa_2024_129997 crossref_primary_10_1016_j_asoc_2024_112012 crossref_primary_10_1016_j_asoc_2025_113550 crossref_primary_10_2478_amns_2024_3120 crossref_primary_10_1016_j_neucom_2025_129557 crossref_primary_10_1016_j_engappai_2024_108641 crossref_primary_10_1016_j_engappai_2025_111264 crossref_primary_10_1016_j_compeleceng_2024_109362 crossref_primary_10_1016_j_ipm_2024_103923 crossref_primary_10_1109_TKDE_2024_3437364 |
| Cites_doi | 10.1016/j.ymeth.2016.08.014 10.1007/s11042-017-5381-7 10.1145/3136625 10.1109/TNNLS.2016.2521602 10.1109/TNNLS.2021.3083763 10.1016/j.neucom.2015.08.104 10.1016/j.patcog.2014.08.004 10.1016/j.patcog.2023.110179 10.1038/44565 10.1016/j.patcog.2021.107873 10.1016/j.ins.2021.11.068 10.1016/j.eswa.2017.11.053 10.1016/j.neucom.2018.04.001 10.1109/TCYB.2013.2272642 10.1016/j.patcog.2014.08.006 10.1016/j.engappai.2020.103855 10.1016/j.knosys.2018.01.009 10.1016/j.knosys.2023.110998 10.1109/TIP.2015.2479560 10.1109/TPAMI.2010.231 10.1109/TCYB.2020.2982445 10.1016/j.ins.2022.11.120 10.1016/j.neucom.2017.11.077 10.1016/j.eswa.2024.123198 10.1016/j.compeleceng.2013.11.024 10.1109/TCYB.2020.3034462 10.1109/ACCESS.2020.3010862 10.1007/s10462-019-09682-y 10.1109/TKDE.2020.2983396 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Inc. |
| Copyright_xml | – notice: 2024 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2024.120277 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| EISSN | 1872-6291 |
| ExternalDocumentID | 10_1016_j_ins_2024_120277 S0020025524001907 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABTAH ABUCO ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- 77I 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c297t-9c7044a80c648f6b9780c75af78ec294aa3be2b772f153a1a900d9450b25a7593 |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001184753500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Tue Nov 18 21:59:37 EST 2025 Sat Nov 29 07:29:04 EST 2025 Sat Mar 02 16:00:32 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Pseudo-supervised learning Self-representation learning Unsupervised feature selection Encoder-decoder model Nonnegative matrix factorization |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c297t-9c7044a80c648f6b9780c75af78ec294aa3be2b772f153a1a900d9450b25a7593 |
| ORCID | 0000-0002-0300-9233 0000-0003-2718-7146 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ins_2024_120277 crossref_primary_10_1016_j_ins_2024_120277 elsevier_sciencedirect_doi_10_1016_j_ins_2024_120277 |
| PublicationCentury | 2000 |
| PublicationDate | March 2024 2024-03-00 |
| PublicationDateYYYYMMDD | 2024-03-01 |
| PublicationDate_xml | – month: 03 year: 2024 text: March 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2024 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Li, Cheng, Wang, Morstatter, Trevino, Tang, Liu (br0040) 2017; 50 Wang, Pedrycz, Zhu, Zhu (br0220) 2015; 48 Cai, He, Han, Huang (br0280) 2011; 33 Seyedi, Ghodsi, Akhlaghian, Jalili, Moradi (br0440) 2019; vol. 101 Baldi (br0210) 2012; vol. 27 Li, Wang, Ruiz (br0020) 2022; 52 Zhao, Du, Wei, Fan (br0350) 2020; 8 Chandrashekar, Sahin (br0060) 2014; 40 Qian, Zhai (br0170) 2013 Wang, Bian, Nie, Li (br0370) 2022; 34 Du, Shen (br0190) 2015 Shajarian, Seyedi, Moradi (br0470) 2017 Tang, Liu, Li, Wang, Chen, Wang, Li (br0410) 2018; 145 Mahmoodi, Seyedi, Akhlaghian Tab, Abdollahpouri (br0480) 2023; 280 Lin, Guan, Chen, Zeng (br0360) 2022; 33 Solorio-Fernández, Carrasco-Ochoa, Martínez-Trinidad (br0070) 2020; 53 Yuan, You, He, Li (br0090) 2022; 52 Hajiveiseh, Seyedi, Akhlaghian Tab (br0490) 2024; 148 Zare, Parsa, Ghatee, Alizadeh (br0340) 2020 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos (br0010) 2015 Shi, Du, Shen (br0310) 2014 Lee, Seung (br0230) 1999; 401 Li, Yang, Liu, Zhou, Lu (br0110) 2012 Hou, Nie, Yi, Wu (br0180) 2011 Tang, Zhu, Chen, Wang, Liu, Tian (br0390) 2018; 96 Shang, Wang, Shang, Jiao, Li (br0260) 2021; 114 Zhu, Li, Zhang, Ju, Wu (br0330) 2017; 28 Yuan, Oja (br0420) 2005 Parsa, Zare, Ghatee (br0150) 2020; 95 Li, Tang (br0160) 2015; 24 Cai, Luo, Wang, Yang (br0030) 2018; 300 Seyedi, Akhlaghian Tab, Lotfi, Salahian, Chavoshinejad (br0430) 2023; 621 Abdollahi, Amjad Seyedi, Reza Noorimehr (br0450) 2020 Wang, Tang, Liu (br0320) 2015 Faraji, Seyedi, Akhlaghian Tab, Mahmoodi (br0500) 2024; 246 Lu, Li, Dong (br0380) 2018; 301 Wang, Wang, Chang (br0050) 2016; 111 Luo, Zheng, Li, Chen, Huang, Peng (br0270) 2022; 586 Cai, Zhang, He (br0120) 2010 Lei, Zhu (br0400) 2018; 77 Wang, Yao, Zhao (br0200) 2016; 184 He, Cai, Niyogi (br0080) 2005; vol. 18 Zhu, Zuo, Zhang, Hu, Shiu (br0100) 2015; 48 Zhao, Wang, Liu (br0300) 2010 Sun, Shen, Gao, Ouyang, Cheng (br0250) 2017 Hou, Nie, Li, Yi, Wu (br0140) 2014; 44 Yang, Shen, Ma, Huang, Zhou (br0290) 2011; vol. Volume Two Ding, Li, Peng, Park (br0240) 2006 Fan, Chang, Zhang, Wang, Du (br0130) 2017 Seyedi, Moradi, Tab (br0460) 2017 Fan (10.1016/j.ins.2024.120277_br0130) 2017 Hajiveiseh (10.1016/j.ins.2024.120277_br0490) 2024; 148 Yang (10.1016/j.ins.2024.120277_br0290) 2011; vol. Volume Two Hou (10.1016/j.ins.2024.120277_br0140) 2014; 44 Li (10.1016/j.ins.2024.120277_br0110) 2012 Chandrashekar (10.1016/j.ins.2024.120277_br0060) 2014; 40 Wang (10.1016/j.ins.2024.120277_br0220) 2015; 48 Shang (10.1016/j.ins.2024.120277_br0260) 2021; 114 Tang (10.1016/j.ins.2024.120277_br0390) 2018; 96 Seyedi (10.1016/j.ins.2024.120277_br0460) 2017 Li (10.1016/j.ins.2024.120277_br0040) 2017; 50 He (10.1016/j.ins.2024.120277_br0080) 2005; vol. 18 Zhu (10.1016/j.ins.2024.120277_br0100) 2015; 48 Li (10.1016/j.ins.2024.120277_br0020) 2022; 52 Yuan (10.1016/j.ins.2024.120277_br0420) 2005 Shajarian (10.1016/j.ins.2024.120277_br0470) 2017 Parsa (10.1016/j.ins.2024.120277_br0150) 2020; 95 Zhao (10.1016/j.ins.2024.120277_br0300) 2010 Lei (10.1016/j.ins.2024.120277_br0400) 2018; 77 Abdollahi (10.1016/j.ins.2024.120277_br0450) 2020 Bolón-Canedo (10.1016/j.ins.2024.120277_br0010) 2015 Wang (10.1016/j.ins.2024.120277_br0370) 2022; 34 Yuan (10.1016/j.ins.2024.120277_br0090) 2022; 52 Qian (10.1016/j.ins.2024.120277_br0170) 2013 Cai (10.1016/j.ins.2024.120277_br0030) 2018; 300 Mahmoodi (10.1016/j.ins.2024.120277_br0480) 2023; 280 Faraji (10.1016/j.ins.2024.120277_br0500) 2024; 246 Du (10.1016/j.ins.2024.120277_br0190) 2015 Solorio-Fernández (10.1016/j.ins.2024.120277_br0070) 2020; 53 Wang (10.1016/j.ins.2024.120277_br0320) 2015 Wang (10.1016/j.ins.2024.120277_br0050) 2016; 111 Shi (10.1016/j.ins.2024.120277_br0310) 2014 Sun (10.1016/j.ins.2024.120277_br0250) 2017 Lu (10.1016/j.ins.2024.120277_br0380) 2018; 301 Ding (10.1016/j.ins.2024.120277_br0240) 2006 Seyedi (10.1016/j.ins.2024.120277_br0430) 2023; 621 Zare (10.1016/j.ins.2024.120277_br0340) 2020 Lee (10.1016/j.ins.2024.120277_br0230) 1999; 401 Wang (10.1016/j.ins.2024.120277_br0200) 2016; 184 Cai (10.1016/j.ins.2024.120277_br0120) 2010 Zhu (10.1016/j.ins.2024.120277_br0330) 2017; 28 Baldi (10.1016/j.ins.2024.120277_br0210) 2012; vol. 27 Lin (10.1016/j.ins.2024.120277_br0360) 2022; 33 Tang (10.1016/j.ins.2024.120277_br0410) 2018; 145 Luo (10.1016/j.ins.2024.120277_br0270) 2022; 586 Seyedi (10.1016/j.ins.2024.120277_br0440) 2019; vol. 101 Cai (10.1016/j.ins.2024.120277_br0280) 2011; 33 Zhao (10.1016/j.ins.2024.120277_br0350) 2020; 8 Li (10.1016/j.ins.2024.120277_br0160) 2015; 24 Hou (10.1016/j.ins.2024.120277_br0180) 2011 |
| References_xml | – volume: vol. 18 year: 2005 ident: br0080 article-title: Laplacian Score for Feature Selection publication-title: Advances in Neural Information Processing Systems – volume: 148 year: 2024 ident: br0490 article-title: Deep asymmetric nonnegative matrix factorization for graph clustering publication-title: Pattern Recognit. – volume: 111 start-page: 21 year: 2016 end-page: 31 ident: br0050 article-title: Feature selection methods for big data bioinformatics: a survey from the search perspective publication-title: Methods – volume: 48 start-page: 10 year: 2015 end-page: 19 ident: br0220 article-title: Subspace learning for unsupervised feature selection via matrix factorization publication-title: Pattern Recognit. – volume: 44 start-page: 793 year: 2014 end-page: 804 ident: br0140 article-title: Joint embedding learning and sparse regression: a framework for unsupervised feature selection publication-title: IEEE Trans. Cybern. – volume: vol. 101 start-page: 790 year: 2019 end-page: 805 ident: br0440 article-title: Self-paced multi-label learning with diversity publication-title: Proceedings of the Eleventh Asian Conference on Machine Learning – volume: 52 start-page: 5522 year: 2022 end-page: 5534 ident: br0090 article-title: Convex non-negative matrix factorization with adaptive graph for unsupervised feature selection publication-title: IEEE Trans. Cybern. – volume: 95 year: 2020 ident: br0150 article-title: Unsupervised feature selection based on adaptive similarity learning and subspace clustering publication-title: Eng. Appl. Artif. Intell. – volume: 300 start-page: 70 year: 2018 end-page: 79 ident: br0030 article-title: Feature selection in machine learning: a new perspective publication-title: Neurocomputing – volume: 401 start-page: 788 year: 1999 end-page: 791 ident: br0230 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature – volume: 33 start-page: 1548 year: 2011 end-page: 1560 ident: br0280 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 8 start-page: 133128 year: 2020 end-page: 133143 ident: br0350 article-title: Local sensitive dual concept factorization for unsupervised feature selection publication-title: IEEE Access – volume: 33 start-page: 6881 year: 2022 end-page: 6892 ident: br0360 article-title: Unsupervised feature selection via orthogonal basis clustering and local structure preserving publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 213 year: 2017 end-page: 218 ident: br0460 article-title: A weakly-supervised factorization method with dynamic graph embedding publication-title: 2017 Artificial Intelligence and Signal Processing Conference (AISP) – start-page: 333 year: 2010 end-page: 342 ident: br0120 article-title: Unsupervised feature selection for multi-cluster data publication-title: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 77 start-page: 29605 year: 2018 end-page: 29622 ident: br0400 article-title: Unsupervised feature selection via local structure learning and sparse learning publication-title: Multimed. Tools Appl. – volume: 50 start-page: 1 year: 2017 end-page: 45 ident: br0040 article-title: Feature selection: a data perspective publication-title: ACM Comput. Surv. – volume: 280 year: 2023 ident: br0480 article-title: Link prediction by adversarial nonnegative matrix factorization publication-title: Knowl.-Based Syst. – year: 2015 ident: br0010 article-title: Feature Selection for High-Dimensional Data – start-page: 977 year: 2014 end-page: 982 ident: br0310 article-title: Robust spectral learning for unsupervised feature selection publication-title: 2014 IEEE International Conference on Data Mining – start-page: 673 year: 2010 end-page: 678 ident: br0300 article-title: Efficient spectral feature selection with minimum redundancy publication-title: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence – volume: 34 start-page: 942 year: 2022 end-page: 953 ident: br0370 article-title: Unsupervised discriminative projection for feature selection publication-title: IEEE Trans. Knowl. Data Eng. – volume: 145 start-page: 109 year: 2018 end-page: 120 ident: br0410 article-title: Robust unsupervised feature selection via dual self-representation and manifold regularization publication-title: Knowl.-Based Syst. – volume: 28 start-page: 1263 year: 2017 end-page: 1275 ident: br0330 article-title: Robust joint graph sparse coding for unsupervised spectral feature selection publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: vol. Volume Two start-page: 1589 year: 2011 end-page: 1594 ident: br0290 article-title: L2, 1-norm regularized discriminative feature selection for unsupervised learning publication-title: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence – volume: vol. 27 start-page: 37 year: 2012 end-page: 49 ident: br0210 article-title: Autoencoders, unsupervised learning, and deep architectures publication-title: Proceedings of ICML Workshop on Unsupervised and Transfer Learning – start-page: 2241 year: 2017 end-page: 2246 ident: br0470 article-title: A clustering-based matrix factorization method to improve the accuracy of recommendation systems publication-title: 2017 Iranian Conference on Electrical Engineering (ICEE) – volume: 246 year: 2024 ident: br0500 article-title: Multi-label feature selection with global and local label correlation publication-title: Expert Syst. Appl. – start-page: 1026 year: 2012 end-page: 1032 ident: br0110 article-title: Unsupervised feature selection using nonnegative spectral analysis publication-title: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence – volume: 621 start-page: 562 year: 2023 end-page: 579 ident: br0430 article-title: Elastic adversarial deep nonnegative matrix factorization for matrix completion publication-title: Inf. Sci. – start-page: 1621 year: 2013 end-page: 1627 ident: br0170 article-title: Robust unsupervised feature selection publication-title: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence – volume: 53 start-page: 907 year: 2020 end-page: 948 ident: br0070 article-title: A review of unsupervised feature selection methods publication-title: Artif. Intell. Rev. – start-page: 1646 year: 2017 end-page: 1653 ident: br0130 article-title: Top-k supervise feature selection via admm for integer programming publication-title: Proceedings of the 26th International Joint Conference on Artificial Intelligence – volume: 40 start-page: 16 year: 2014 end-page: 28 ident: br0060 article-title: A survey on feature selection methods publication-title: 40th-Year Commemorative Issue – volume: 301 start-page: 36 year: 2018 end-page: 45 ident: br0380 article-title: Structure preserving unsupervised feature selection publication-title: Neurocomputing – start-page: 323 year: 2020 end-page: 328 ident: br0450 article-title: Asymmetric semi-nonnegative matrix factorization for directed graph clustering publication-title: 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) – start-page: 1324 year: 2011 end-page: 1329 ident: br0180 article-title: Feature selection via joint embedding learning and sparse regression publication-title: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Two – volume: 52 start-page: 1642 year: 2022 end-page: 1660 ident: br0020 article-title: A survey on sparse learning models for feature selection publication-title: IEEE Trans. Cybern. – volume: 184 start-page: 232 year: 2016 end-page: 242 ident: br0200 article-title: Auto-encoder based dimensionality reduction publication-title: Neurocomputing – start-page: 597 year: 2017 end-page: 606 ident: br0250 article-title: A non-negative symmetric encoder-decoder approach for community detection publication-title: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management – volume: 48 start-page: 438 year: 2015 end-page: 446 ident: br0100 article-title: Unsupervised feature selection by regularized self-representation publication-title: Pattern Recognit. – start-page: 50 year: 2020 end-page: 55 ident: br0340 article-title: Similarity preserving unsupervised feature selection based on sparse learning publication-title: 2020 10th International Symposium on Telecommunications (IST) – start-page: 209 year: 2015 end-page: 218 ident: br0190 article-title: Unsupervised feature selection with adaptive structure learning publication-title: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 114 year: 2021 ident: br0260 article-title: Dual space latent representation learning for unsupervised feature selection publication-title: Pattern Recognit. – volume: 96 start-page: 64 year: 2018 end-page: 76 ident: br0390 article-title: Robust graph regularized unsupervised feature selection publication-title: Expert Syst. Appl. – volume: 586 start-page: 662 year: 2022 end-page: 675 ident: br0270 article-title: Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving publication-title: Inf. Sci. – start-page: 126 year: 2006 end-page: 135 ident: br0240 article-title: Orthogonal nonnegative matrix t-factorizations for clustering publication-title: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 333 year: 2005 end-page: 342 ident: br0420 article-title: Projective nonnegative matrix factorization for image compression and feature extraction publication-title: Image Analysis – volume: 24 start-page: 5343 year: 2015 end-page: 5355 ident: br0160 article-title: Unsupervised feature selection via nonnegative spectral analysis and redundancy control publication-title: IEEE Trans. Image Process. – start-page: 470 year: 2015 end-page: 476 ident: br0320 article-title: Embedded unsupervised feature selection publication-title: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence – volume: 111 start-page: 21 year: 2016 ident: 10.1016/j.ins.2024.120277_br0050 article-title: Feature selection methods for big data bioinformatics: a survey from the search perspective publication-title: Methods doi: 10.1016/j.ymeth.2016.08.014 – volume: 77 start-page: 29605 issue: 22 year: 2018 ident: 10.1016/j.ins.2024.120277_br0400 article-title: Unsupervised feature selection via local structure learning and sparse learning publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-017-5381-7 – start-page: 126 year: 2006 ident: 10.1016/j.ins.2024.120277_br0240 article-title: Orthogonal nonnegative matrix t-factorizations for clustering – volume: 50 start-page: 1 issue: 6 year: 2017 ident: 10.1016/j.ins.2024.120277_br0040 article-title: Feature selection: a data perspective publication-title: ACM Comput. Surv. doi: 10.1145/3136625 – volume: 28 start-page: 1263 issue: 6 year: 2017 ident: 10.1016/j.ins.2024.120277_br0330 article-title: Robust joint graph sparse coding for unsupervised spectral feature selection publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2521602 – volume: 33 start-page: 6881 issue: 11 year: 2022 ident: 10.1016/j.ins.2024.120277_br0360 article-title: Unsupervised feature selection via orthogonal basis clustering and local structure preserving publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3083763 – volume: 184 start-page: 232 year: 2016 ident: 10.1016/j.ins.2024.120277_br0200 article-title: Auto-encoder based dimensionality reduction publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.104 – volume: 48 start-page: 10 issue: 1 year: 2015 ident: 10.1016/j.ins.2024.120277_br0220 article-title: Subspace learning for unsupervised feature selection via matrix factorization publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.08.004 – volume: 148 year: 2024 ident: 10.1016/j.ins.2024.120277_br0490 article-title: Deep asymmetric nonnegative matrix factorization for graph clustering publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2023.110179 – start-page: 1026 year: 2012 ident: 10.1016/j.ins.2024.120277_br0110 article-title: Unsupervised feature selection using nonnegative spectral analysis – volume: vol. 27 start-page: 37 year: 2012 ident: 10.1016/j.ins.2024.120277_br0210 article-title: Autoencoders, unsupervised learning, and deep architectures – volume: 401 start-page: 788 issue: 6755 year: 1999 ident: 10.1016/j.ins.2024.120277_br0230 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature doi: 10.1038/44565 – volume: 114 year: 2021 ident: 10.1016/j.ins.2024.120277_br0260 article-title: Dual space latent representation learning for unsupervised feature selection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2021.107873 – volume: 586 start-page: 662 year: 2022 ident: 10.1016/j.ins.2024.120277_br0270 article-title: Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.11.068 – start-page: 673 year: 2010 ident: 10.1016/j.ins.2024.120277_br0300 article-title: Efficient spectral feature selection with minimum redundancy – volume: 96 start-page: 64 year: 2018 ident: 10.1016/j.ins.2024.120277_br0390 article-title: Robust graph regularized unsupervised feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.11.053 – volume: 301 start-page: 36 year: 2018 ident: 10.1016/j.ins.2024.120277_br0380 article-title: Structure preserving unsupervised feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.04.001 – start-page: 209 year: 2015 ident: 10.1016/j.ins.2024.120277_br0190 article-title: Unsupervised feature selection with adaptive structure learning – start-page: 470 year: 2015 ident: 10.1016/j.ins.2024.120277_br0320 article-title: Embedded unsupervised feature selection – start-page: 50 year: 2020 ident: 10.1016/j.ins.2024.120277_br0340 article-title: Similarity preserving unsupervised feature selection based on sparse learning – volume: 44 start-page: 793 issue: 6 year: 2014 ident: 10.1016/j.ins.2024.120277_br0140 article-title: Joint embedding learning and sparse regression: a framework for unsupervised feature selection publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2013.2272642 – start-page: 2241 year: 2017 ident: 10.1016/j.ins.2024.120277_br0470 article-title: A clustering-based matrix factorization method to improve the accuracy of recommendation systems – volume: 48 start-page: 438 issue: 2 year: 2015 ident: 10.1016/j.ins.2024.120277_br0100 article-title: Unsupervised feature selection by regularized self-representation publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.08.006 – volume: 95 year: 2020 ident: 10.1016/j.ins.2024.120277_br0150 article-title: Unsupervised feature selection based on adaptive similarity learning and subspace clustering publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103855 – start-page: 977 year: 2014 ident: 10.1016/j.ins.2024.120277_br0310 article-title: Robust spectral learning for unsupervised feature selection – volume: vol. 101 start-page: 790 year: 2019 ident: 10.1016/j.ins.2024.120277_br0440 article-title: Self-paced multi-label learning with diversity – start-page: 333 year: 2005 ident: 10.1016/j.ins.2024.120277_br0420 article-title: Projective nonnegative matrix factorization for image compression and feature extraction – volume: 145 start-page: 109 year: 2018 ident: 10.1016/j.ins.2024.120277_br0410 article-title: Robust unsupervised feature selection via dual self-representation and manifold regularization publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.01.009 – volume: 280 year: 2023 ident: 10.1016/j.ins.2024.120277_br0480 article-title: Link prediction by adversarial nonnegative matrix factorization publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2023.110998 – start-page: 333 year: 2010 ident: 10.1016/j.ins.2024.120277_br0120 article-title: Unsupervised feature selection for multi-cluster data – start-page: 1646 year: 2017 ident: 10.1016/j.ins.2024.120277_br0130 article-title: Top-k supervise feature selection via admm for integer programming – volume: 24 start-page: 5343 issue: 12 year: 2015 ident: 10.1016/j.ins.2024.120277_br0160 article-title: Unsupervised feature selection via nonnegative spectral analysis and redundancy control publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2479560 – year: 2015 ident: 10.1016/j.ins.2024.120277_br0010 – volume: 33 start-page: 1548 issue: 8 year: 2011 ident: 10.1016/j.ins.2024.120277_br0280 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.231 – volume: 52 start-page: 1642 issue: 3 year: 2022 ident: 10.1016/j.ins.2024.120277_br0020 article-title: A survey on sparse learning models for feature selection publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.2982445 – volume: 621 start-page: 562 year: 2023 ident: 10.1016/j.ins.2024.120277_br0430 article-title: Elastic adversarial deep nonnegative matrix factorization for matrix completion publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.11.120 – volume: 300 start-page: 70 year: 2018 ident: 10.1016/j.ins.2024.120277_br0030 article-title: Feature selection in machine learning: a new perspective publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.11.077 – volume: vol. 18 year: 2005 ident: 10.1016/j.ins.2024.120277_br0080 article-title: Laplacian Score for Feature Selection – start-page: 323 year: 2020 ident: 10.1016/j.ins.2024.120277_br0450 article-title: Asymmetric semi-nonnegative matrix factorization for directed graph clustering – volume: 246 year: 2024 ident: 10.1016/j.ins.2024.120277_br0500 article-title: Multi-label feature selection with global and local label correlation publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2024.123198 – volume: 40 start-page: 16 issue: 1 year: 2014 ident: 10.1016/j.ins.2024.120277_br0060 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.024 – volume: 52 start-page: 5522 issue: 6 year: 2022 ident: 10.1016/j.ins.2024.120277_br0090 article-title: Convex non-negative matrix factorization with adaptive graph for unsupervised feature selection publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.3034462 – start-page: 1324 year: 2011 ident: 10.1016/j.ins.2024.120277_br0180 article-title: Feature selection via joint embedding learning and sparse regression – start-page: 597 year: 2017 ident: 10.1016/j.ins.2024.120277_br0250 article-title: A non-negative symmetric encoder-decoder approach for community detection – volume: 8 start-page: 133128 year: 2020 ident: 10.1016/j.ins.2024.120277_br0350 article-title: Local sensitive dual concept factorization for unsupervised feature selection publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010862 – volume: 53 start-page: 907 issue: 2 year: 2020 ident: 10.1016/j.ins.2024.120277_br0070 article-title: A review of unsupervised feature selection methods publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-019-09682-y – volume: vol. Volume Two start-page: 1589 year: 2011 ident: 10.1016/j.ins.2024.120277_br0290 article-title: L2, 1-norm regularized discriminative feature selection for unsupervised learning – start-page: 213 year: 2017 ident: 10.1016/j.ins.2024.120277_br0460 article-title: A weakly-supervised factorization method with dynamic graph embedding – volume: 34 start-page: 942 issue: 2 year: 2022 ident: 10.1016/j.ins.2024.120277_br0370 article-title: Unsupervised discriminative projection for feature selection publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2020.2983396 – start-page: 1621 year: 2013 ident: 10.1016/j.ins.2024.120277_br0170 article-title: Robust unsupervised feature selection |
| SSID | ssj0004766 |
| Score | 2.4892538 |
| Snippet | Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 120277 |
| SubjectTerms | Encoder-decoder model Nonnegative matrix factorization Pseudo-supervised learning Self-representation learning Unsupervised feature selection |
| Title | Unsupervised feature selection using orthogonal encoder-decoder factorization |
| URI | https://dx.doi.org/10.1016/j.ins.2024.120277 |
| Volume | 663 |
| WOSCitedRecordID | wos001184753500001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6291 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004766 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Bb9MwFLZKxwEOCAaIAUM-IA5MQWnixPGxQpsA0YlDJ_UWvTj22tKmVdpO20776Tw7TppVG2IHLmmUOm6U7-vn5-f3ngn56IdaBnkUe1pm0mNoEaEOBrEX-wq0YJpDZhOFf_LT02Q0Er86nZs6F-ZixosiubwUy_8KNV5DsE3q7APgbjrFC3iOoOMRYcfjPwF_Vqw2S6MAK7QltbKFO49WdrsbA_XGOgfMas3i3HoBTSXLXJVeruyn24HHpWe2bVeXuWR7cQNnY5APFtegocpaH0B5BfPGdaOucHy0TlZzdtSfTyFvBHlSoqqMYT63JRImVaz3dAJNyHD_93gG52MjQ0Owa0cnhtNF21sRsG24VpM94HtmHtNW4NhpXKWhPeOP4XfKe-VpmOKcxFRaD9iXbdvbpbR3hrgm8LCOaZum2EVqukirLh6RvYBHIumSvf7349GPbW4tr9a76-euV8ZtjODOc9xt27TsleFz8sxNNGi_IsgL0lHFPnnaKj-5Tw5d0gr9RFvYUif3L8mgTSXqqEQbKlFLJbqlEt2hEr1FpVfk7OR4-PWb57bf8GQg-NoTkvuMQeLLmCU6zkytKskj0DxR2IIBhJkKMpyeaRw2oQfC93PBIj8LIsB3Gb4m3WJRqDeEJtBTOhBoTWrJuAzxawkikyrs6TyK-AHx6xeXSleb3myRMkvvBeyAfG5uWVaFWf7WmNVopO4PUlmMKTLr_tvePuQ33pEnW8K_J911uVGH5LG8WE9W5QdHqz_vqJz_ |
| linkProvider | Elsevier |
| 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=Unsupervised+feature+selection+using+orthogonal+encoder-decoder+factorization&rft.jtitle=Information+sciences&rft.au=Mozafari%2C+Maryam&rft.au=Seyedi%2C+Seyed+Amjad&rft.au=Pir+Mohammadiani%2C+Rojiar&rft.au=Akhlaghian+Tab%2C+Fardin&rft.date=2024-03-01&rft.issn=0020-0255&rft.volume=663&rft.spage=120277&rft_id=info:doi/10.1016%2Fj.ins.2024.120277&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ins_2024_120277 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |