Iterative Pseudo-Sparse Partial Least Square and its Higher-Order variant: Application to inference from high-dimensional biosignals
Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe chal...
Uloženo v:
| Vydáno v: | IEEE transactions on cognitive and developmental systems Ročník 16; číslo 1; s. 1 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2379-8920, 2379-8939 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this paper proposes two PLS-based algorithms, Pseudo-Sparse PLS (PS-PLS) and iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3D trajectory from Electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the Electromyography (EMG) powers from recorded Magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the High-Density surface Electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches. |
|---|---|
| AbstractList | Partial least square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this article proposes two PLS-based algorithms, pseudo-sparse PLS (PS-PLS) and iterative pseudo-sparse higher order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3-D trajectory from electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the electromyography (EMG) powers from recorded magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the high-density surface electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches. Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this paper proposes two PLS-based algorithms, Pseudo-Sparse PLS (PS-PLS) and iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3D trajectory from Electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the Electromyography (EMG) powers from recorded Magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the High-Density surface Electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches. |
| Author | Einizade, Aref Sardouie, Sepideh Hajipour |
| Author_xml | – sequence: 1 givenname: Aref orcidid: 0000-0002-8546-7261 surname: Einizade fullname: Einizade, Aref organization: Electrical Engineering department, Sharif University of Technology, Tehran, Iran – sequence: 2 givenname: Sepideh Hajipour orcidid: 0000-0003-0594-3019 surname: Sardouie fullname: Sardouie, Sepideh Hajipour organization: Electrical Engineering department, Sharif University of Technology, Tehran, Iran |
| BookMark | eNp9kE1rGzEQhkVJIWmaHxDoQdDzuhppvZJ6C27zAYYUnJ4XWZpNFOzVZiQHes8Pj1yHUnroaT54n5eZ9wM7GtOIjJ2DmAEI--Vu8W01k0KqmZKdFiDesROptG2MVfboTy_FMTvL-VEIAZ3SptUn7OWmILkSn5H_yLgLqVlNjnKdHJXoNnyJLhe-eto5Qu7GwGPJ_DrePyA1txSQ-LOj6MbylV9M0yb6apZGXhKP44CEo0c-UNryh8o0IW5xzFVQndcx5Xhfu_yRvR9qwbO3esp-Xn6_W1w3y9urm8XFsvHStqWZg1Bad7p1nQzdINet8WD8HDoLeh60CVL71rdDO-hhLY1yJhhtQbq6huDVKft88J0oPe0wl_4x7Wh_QS-tBNkZBVBV-qDylHImHHofy--vCrm46UH0-9T7fer9PvX-LfVKwj_kRHHr6Nd_mU8HJiLiX3oQUnZWvQLg4JBn |
| CODEN | ITCDA4 |
| CitedBy_id | crossref_primary_10_1088_1741_2552_ade917 crossref_primary_10_3390_bios14050221 |
| Cites_doi | 10.1088/1741-2560/9/4/045010 10.1038/s41598-017-16579-9 10.1109/ACCESS.2020.3019267 10.1109/TSP.2017.2690524 10.1111/j.2517-6161.1996.tb02080.x 10.1002/sam.11169 10.1109/TBME.2017.2768442 10.1002/cem.1236 10.1088/1741-2552/ac3314 10.1016/j.jneumeth.2016.06.011 10.2307/1267352 10.1371/journal.pone.0154878 10.1016/0003-2670(86)80028-9 10.1016/j.jphysparis.2017.03.002 10.1137/S0895479897326432 10.1111/j.1467-9868.2009.00723.x 10.1016/0169-7439(93)85002-X 10.3389/fneng.2010.00003 10.1016/j.ijforecast.2006.03.001 10.1016/j.jneumeth.2015.03.018 10.1088/1741-2560/11/6/066005 10.1016/j.patcog.2014.12.002 10.1109/IJCNN.2019.8852214 10.1214/009053604000000067 10.1371/journal.pone.0072085 10.1088/1741-2552/aab290 10.1007/s12021-020-09455-x 10.1088/1741-2560/9/3/036015 10.1007/978-0-387-84858-7 10.1016/j.patcog.2017.06.004 10.1007/s10910-015-0570-y 10.1109/TNSRE.2021.3082551 10.1523/JNEUROSCI.4882-10.2011 10.3389/fncom.2020.00022 10.1109/ACCESS.2021.3123098 10.1016/j.neuroimage.2020.116893 10.1109/TPAMI.2012.254 10.1088/1741-2560/9/2/026017 10.1007/978-3-319-53547-0_39 10.1007/978-1-4615-7566-5 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/TCDS.2023.3267010 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 2379-8939 |
| EndPage | 1 |
| ExternalDocumentID | 10_1109_TCDS_2023_3267010 10102269 |
| Genre | orig-research |
| GroupedDBID | 0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFS AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS IFIPE IPLJI JAVBF M43 O9- OCL RIA RIE AAYXX AGSQL CITATION EJD 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c294t-510377674a62d6f2b48c18c5169175d78d27c4c4f4f7fb283a8d87912a7c41dc3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001167556100023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2379-8920 |
| IngestDate | Mon Jun 30 10:16:24 EDT 2025 Sat Nov 29 02:22:11 EST 2025 Tue Nov 18 21:36:21 EST 2025 Wed Aug 27 02:14:02 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c294t-510377674a62d6f2b48c18c5169175d78d27c4c4f4f7fb283a8d87912a7c41dc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-8546-7261 0000-0003-0594-3019 |
| PQID | 2921268311 |
| PQPubID | 85513 |
| PageCount | 1 |
| ParticipantIDs | crossref_primary_10_1109_TCDS_2023_3267010 crossref_citationtrail_10_1109_TCDS_2023_3267010 proquest_journals_2921268311 ieee_primary_10102269 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-02-01 |
| PublicationDateYYYYMMDD | 2024-02-01 |
| PublicationDate_xml | – month: 02 year: 2024 text: 2024-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE transactions on cognitive and developmental systems |
| PublicationTitleAbbrev | TCDS |
| PublicationYear | 2024 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref36 Rosipal (ref32) 2001; 2 ref30 ref11 ref33 ref10 Schmidt (ref44) 2005 ref2 ref17 ref39 ref16 ref38 ref19 ref18 Hastie (ref31) 2009; 2 ref24 ref23 Theodoridis (ref1) 2015 ref26 ref25 ref20 ref42 ref41 ref22 Schmidt (ref9) 2009 ref21 ref43 Geladi (ref8) 1986; 185 ref28 ref27 ref29 ref7 ref4 ref3 ref6 ref5 ref40 |
| References_xml | – ident: ref15 doi: 10.1088/1741-2560/9/4/045010 – ident: ref26 doi: 10.1038/s41598-017-16579-9 – volume-title: Machine Learning: A Bayesian and Optimization Perspective year: 2015 ident: ref1 – ident: ref24 doi: 10.1109/ACCESS.2020.3019267 – ident: ref35 doi: 10.1109/TSP.2017.2690524 – ident: ref7 doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: ref11 doi: 10.1002/sam.11169 – ident: ref21 doi: 10.1109/TBME.2017.2768442 – volume: 2 start-page: 97 year: 2001 ident: ref32 article-title: Kernel partial least squares regression in reproducing kernel Hilbert space publication-title: J. Mach. Learn. Res. – ident: ref34 doi: 10.1002/cem.1236 – ident: ref39 doi: 10.1088/1741-2552/ac3314 – ident: ref42 doi: 10.1016/j.jneumeth.2016.06.011 – ident: ref6 doi: 10.2307/1267352 – ident: ref33 doi: 10.1371/journal.pone.0154878 – volume: 185 start-page: 1 year: 1986 ident: ref8 article-title: Partial least-squares regression: A tutorial publication-title: Analytica Chimica Acta doi: 10.1016/0003-2670(86)80028-9 – ident: ref18 doi: 10.1016/j.jphysparis.2017.03.002 – ident: ref5 doi: 10.1137/S0895479897326432 – ident: ref12 doi: 10.1111/j.1467-9868.2009.00723.x – ident: ref17 doi: 10.1016/0169-7439(93)85002-X – ident: ref37 doi: 10.3389/fneng.2010.00003 – ident: ref38 doi: 10.1016/j.ijforecast.2006.03.001 – ident: ref28 doi: 10.1016/j.jneumeth.2015.03.018 – ident: ref36 doi: 10.1088/1741-2560/11/6/066005 – ident: ref3 doi: 10.1016/j.patcog.2014.12.002 – ident: ref19 doi: 10.1109/IJCNN.2019.8852214 – ident: ref43 doi: 10.1214/009053604000000067 – ident: ref22 doi: 10.1371/journal.pone.0072085 – ident: ref25 doi: 10.1088/1741-2552/aab290 – ident: ref41 doi: 10.1007/s12021-020-09455-x – ident: ref23 doi: 10.1088/1741-2560/9/3/036015 – volume: 2 start-page: 1 volume-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction year: 2009 ident: ref31 doi: 10.1007/978-0-387-84858-7 – ident: ref4 doi: 10.1016/j.patcog.2017.06.004 – year: 2009 ident: ref9 article-title: Optimization methods for l1-regularization – ident: ref16 doi: 10.1007/s10910-015-0570-y – ident: ref30 doi: 10.1109/TNSRE.2021.3082551 – ident: ref29 doi: 10.1523/JNEUROSCI.4882-10.2011 – ident: ref20 doi: 10.3389/fncom.2020.00022 – ident: ref14 doi: 10.1109/ACCESS.2021.3123098 – year: 2005 ident: ref44 article-title: Least squares optimization with l1-norm regularization – ident: ref40 doi: 10.1016/j.neuroimage.2020.116893 – ident: ref10 doi: 10.1109/TPAMI.2012.254 – ident: ref13 doi: 10.1088/1741-2560/9/2/026017 – ident: ref27 doi: 10.1007/978-3-319-53547-0_39 – ident: ref2 doi: 10.1007/978-1-4615-7566-5 |
| SSID | ssj0001637847 |
| Score | 2.3119786 |
| Snippet | Partial Least Square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem... Partial least square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Feature extraction Fingers Iterative algorithms Iterative methods iterative Pseudo-Sparse Higher-Order PLS (iPS-HOPLS) Least squares Linear regression Machine Learning Magnetoencephalography Optimization Partial Least Square (PLS) Prediction algorithms Pseudo-Sparse PLS (PS-PLS) Regression Regression coefficients Task analysis Tensors |
| Title | Iterative Pseudo-Sparse Partial Least Square and its Higher-Order variant: Application to inference from high-dimensional biosignals |
| URI | https://ieeexplore.ieee.org/document/10102269 https://www.proquest.com/docview/2921268311 |
| Volume | 16 |
| WOSCitedRecordID | wos001167556100023&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2379-8939 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001637847 issn: 2379-8920 databaseCode: RIE dateStart: 20160101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEA66ePDiW1xdJQfxIGTtI83D2-IDBVFhFbyVNklhQdvV7Qre_eHOpF0VRMFbW5JQ-CaZmczMN4TsZ7oIC5vlzIDlBg6KCpg2UrLcxiKIcg5KzhcKX8nra_XwoG_bYnVfC-Oc88lnro-PPpZvKzPFqzLY4eifCD1P5qUUTbHW14WKiKXyDcWiWGqmdDSLYoaBPro7OR32sVV4H-wVGWDB7Dc95Bur_DiNvYo5X_7nz62QpdaWpIMG_FUy58o1sj4owY9-eqMH1Gd3-mvzdfJ-6emT4WyjtxM3tRUbjsGnhTeUHVjlCpv40OEziIyjWWnpqJ7QJguE3SA_J30FtxpwOKaDr6A3rSs6mtUMUqxVoUiAzCw2DWgIP2g-qjBLBOR8g9yfn92dXLC2AwMzkeY1Q7o9T_eTiciKArBTJlQGY2tgdlipbCQNN7zghSxysFQyZZXUYZTB59CaeJN0yqp0W4RmPC9EIrlwPOHGJVmowRYskhwM0ERJ1SXBDI_UtPTk2CXjMfVuSqBThDBFCNMWwi45_Jwybrg5_hq8gZh9G9jA1SW9Geppu30naaRBowsVh-H2L9N2yCKszpv87R7p1C9Tt0sWzGs9mrzsecn8AKl14Do |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9swED-2brC-dB9tadpu08PYQ0GpP2RL2lvoVhKWZYVk0DdjSzIEWjttnELf-4f3TnbWQllhb7aRbMPvpLvTffwAvuS6DEubF9yg5YYOigq4NlLywsZpEBUClZwvFB7LyUSdn-uzrljd18I453zymevTpY_l29qs6KgMVzj5J6l-Ca-IOqsr13o4UkljqTylWBRLzZWO1nHMMNDHs5Pv0z6RhffRYpEBlcw-0kSeWuXJfuyVzOnb__y9d7DVWZNs0ML_Hl646gNsDyr0pC9v2Vfm8zv9wfk23I18A2Xc3djZ0q1szacL9GrxjqQH3zImGh82vUKhcSyvLJs3S9bmgfDf1KGT3aBjjUh8Y4OHsDdrajZfVw0yqlZh1AKZW6INaFt-sGJeU54ISvoO_Dn9MTsZ8o6DgZtIi4ZTwz3f8CdPI5uWiJ4yoTIUXUPDw0plI2mEEaUoZVmgrZIrq6QOoxwfh9bEu7BR1ZXbA5aLokwTKVInEmFckocarcEyKdAETZRUPQjWeGSma1BOPBkXmXdUAp0RhBlBmHUQ9uDo75RF253jucE7hNmjgS1cPThco551C3iZRRp1eqriMNz_x7TP8GY4-zXOxqPJzwPYxC-JNpv7EDaa65X7CK_NTTNfXn_yUnoPftbjgw |
| 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=Iterative+Pseudo-Sparse+Partial+Least+Square+and+Its+Higher+Order+Variant%3A+Application+to+Inference+From+High-Dimensional+Biosignals&rft.jtitle=IEEE+transactions+on+cognitive+and+developmental+systems&rft.au=Einizade%2C+Aref&rft.au=Sardouie%2C+Sepideh+Hajipour&rft.date=2024-02-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=2379-8920&rft.eissn=2379-8939&rft.volume=16&rft.issue=1&rft.spage=296&rft_id=info:doi/10.1109%2FTCDS.2023.3267010&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2379-8920&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2379-8920&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2379-8920&client=summon |