Joint Principal Component Analysis and Supervised k Means Algorithm via Non-Iterative Analytic Optimization Approach
It is worth noting that the traditional methods for performing both the dimensional reduction and the classification are via the two steps iterative approaches. In this case, performing the dimensional reduction does not consider the classification. On the other hand, the classification is performed...
Uloženo v:
| Vydáno v: | IEEE transactions on signal processing Ročník 72; s. 1348 - 1360 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1053-587X, 1941-0476 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | It is worth noting that the traditional methods for performing both the dimensional reduction and the classification are via the two steps iterative approaches. In this case, performing the dimensional reduction does not consider the classification. On the other hand, the classification is performed in the original feature domain and it does not consider the dimensional reduction. Here, the transform matrix only takes an effect on the dimensional reduction, but not on the classification. The synergy between the dimensional reduction and the classification has been ignored. As a result, the overall performance is not optimal. To address this issue, this paper proposes a joint principal component analysis (PCA) and supervised k means approach for performing the dimensional reduction and the classification simultaneously. In particular, both the reconstruction error due to the dimensional reduction as well as the total distance between the cluster centers and the feature vectors in the transformed domain are minimized subject to the unitary condition of the transform matrix. Here, we have two decision variables. They are the transform matrix and the cluster centers, instead of a single decision variable in each iteration in the traditional iterative method. To find the analytical solution of the optimization problem, the first order derivative condition of the optimization problem is first expressed as the matrix equations. However, there is a structure deficiency on the matrix equation. To address this issue, this paper employs the property of the singular matrices of the symmetric matrix for solving these matrix equations with the guarantee of the satisfaction of the structural deficiency. As a result, the analytical form of the solutions is derived. The proposed method is evaluated via performing the mental arithmetic classification based on the electroencephalograms (EEGs) downloaded from the PhysioNet database. The comparisons to the state of the art algorithms for performing the mental arithmetic classification and the conventional methods for finding the solutions of the constrained optimization problems are conducted. The results demonstrate that our proposed method achieves the higher accuracy and requires the lower execution time. This validates the effectiveness and the efficiency of our proposed method. |
|---|---|
| AbstractList | It is worth noting that the traditional methods for performing both the dimensional reduction and the classification are via the two steps iterative approaches. In this case, performing the dimensional reduction does not consider the classification. On the other hand, the classification is performed in the original feature domain and it does not consider the dimensional reduction. Here, the transform matrix only takes an effect on the dimensional reduction, but not on the classification. The synergy between the dimensional reduction and the classification has been ignored. As a result, the overall performance is not optimal. To address this issue, this paper proposes a joint principal component analysis (PCA) and supervised k means approach for performing the dimensional reduction and the classification simultaneously. In particular, both the reconstruction error due to the dimensional reduction as well as the total distance between the cluster centers and the feature vectors in the transformed domain are minimized subject to the unitary condition of the transform matrix. Here, we have two decision variables. They are the transform matrix and the cluster centers, instead of a single decision variable in each iteration in the traditional iterative method. To find the analytical solution of the optimization problem, the first order derivative condition of the optimization problem is first expressed as the matrix equations. However, there is a structure deficiency on the matrix equation. To address this issue, this paper employs the property of the singular matrices of the symmetric matrix for solving these matrix equations with the guarantee of the satisfaction of the structural deficiency. As a result, the analytical form of the solutions is derived. The proposed method is evaluated via performing the mental arithmetic classification based on the electroencephalograms (EEGs) downloaded from the PhysioNet database. The comparisons to the state of the art algorithms for performing the mental arithmetic classification and the conventional methods for finding the solutions of the constrained optimization problems are conducted. The results demonstrate that our proposed method achieves the higher accuracy and requires the lower execution time. This validates the effectiveness and the efficiency of our proposed method. |
| Author | Ling, Bingo Wing-Kuen Zhang, Zhanbin Huang, Guoheng |
| Author_xml | – sequence: 1 givenname: Zhanbin orcidid: 0009-0008-3825-0503 surname: Zhang fullname: Zhang, Zhanbin organization: School of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong Province, China – sequence: 2 givenname: Bingo Wing-Kuen orcidid: 0000-0002-0633-7224 surname: Ling fullname: Ling, Bingo Wing-Kuen email: yongquanling@gdut.edu.cn organization: School of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong Province, China – sequence: 3 givenname: Guoheng orcidid: 0000-0002-3640-3229 surname: Huang fullname: Huang, Guoheng organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong Province, China |
| BookMark | eNpNkM1rAjEQxUOxULW999BDoOe1ySbZ3RxF-mGxVdBCb0s2m62xbrJNomD_-kb0UBiY4fHezPAbgJ6xRgFwi9EIY8QfVsvFKEUpHRGSMU7pBehjTnGCaJ714owYSViRf16BgfcbhDClPOuD8Gq1CXDhtJG6E1s4sW0XN0dtbMT24LWHwtRwueuU22uvavgN35QwHo63X9bpsG7hXgv4bk0yDcqJoPfqlA1awnkXdKt_o2oNHHeds0Kur8FlI7Ze3Zz7EHw8Pa4mL8ls_jydjGeJTCkLSV3xJkO4KmRFFUOMiUIpjqtGVKShEiFCVCPqjGaS5nnOOJeyyAmveV0XIm3IENyf9sazPzvlQ7mxOxdf82XKM45YmuZ5dKGTSzrrvVNN2TndCncoMSqPbMvItjyyLc9sY-TuFNFKqX92SrJY5A8jznmE |
| CODEN | ITPRED |
| Cites_doi | 10.1016/j.patrec.2014.11.017 10.1016/j.neucom.2005.06.021 10.1109/tcsi.2004.834493 10.3390/electronics10091079 10.3390/e23080931 10.1016/j.cosrev.2021.100378 10.1109/tpami.2004.1261097 10.1016/j.eswa.2020.114350 10.1109/tsp.2008.2008254 10.1016/j.compeleceng.2022.107684 10.1023/a:1026065325419 10.1109/tnn.2006.873281 10.1145/1015330.1015408 10.1155/2018/9385947 10.3390/data4010014 10.1016/j.patrec.2004.01.011 10.1007/s521-001-8051-z 10.1109/tim.2023.3265114 10.1109/tsp.2020.3001906 10.1109/embc.2013.6611107 10.3390/e21040376 10.1007/s10107-012-0584-1 10.1145/1553374.1553501 10.1109/tnnls.2022.3159573 10.1080/00949655.2017.1327588 10.1561/2200000058 10.1016/j.procs.2019.01.008 10.1561/2200000016 10.23919/eusipco47968.2020.9287358 10.1007/s00357-019-09349-x |
| 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/TSP.2024.3365944 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) Online 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 Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1941-0476 |
| EndPage | 1360 |
| ExternalDocumentID | 10_1109_TSP_2024_3365944 10436436 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent grantid: 501130144 – fundername: Hong Kong Innovation and Technology Commission, Enterprise Support Scheme grantid: S/E/070/17 – fundername: National Natural Science Foundation of China; National Nature Science Foundation of China grantid: U1701266; 61671163; 62071128; 61901123 funderid: 10.13039/501100001809 – fundername: Team Project of the Education Ministry of the Guangdong Province grantid: 2017KCXTD011 |
| GroupedDBID | -~X .DC 0R~ 29I 3EH 4.4 53G 5GY 5VS 6IK 85S 97E AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACIWK ACKIV ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AJQPL AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 E.L EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c245t-db9f601b8cb4e5055a8ee91bfab3f4c0033efad646c4777599cc8739d9dd8a2f3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001188290200007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-587X |
| IngestDate | Mon Jun 30 08:33:32 EDT 2025 Sat Nov 29 04:10:58 EST 2025 Wed Aug 27 02:17:01 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| 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-c245t-db9f601b8cb4e5055a8ee91bfab3f4c0033efad646c4777599cc8739d9dd8a2f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-0633-7224 0000-0002-3640-3229 0009-0008-3825-0503 |
| PQID | 2969052277 |
| PQPubID | 85478 |
| PageCount | 13 |
| ParticipantIDs | crossref_primary_10_1109_TSP_2024_3365944 proquest_journals_2969052277 ieee_primary_10436436 |
| PublicationCentury | 2000 |
| PublicationDate | 20240000 2024-00-00 20240101 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – year: 2024 text: 20240000 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on signal processing |
| PublicationTitleAbbrev | TSP |
| 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 ref12 ref15 Kungurtsev (ref18) 2019 ref14 ref31 ref30 ref11 ref10 ref2 ref1 ref17 ref16 ref19 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref9 doi: 10.1016/j.patrec.2014.11.017 – ident: ref25 doi: 10.1016/j.neucom.2005.06.021 – ident: ref20 doi: 10.1109/tcsi.2004.834493 – ident: ref26 doi: 10.3390/electronics10091079 – ident: ref7 doi: 10.3390/e23080931 – ident: ref6 doi: 10.1016/j.cosrev.2021.100378 – ident: ref1 doi: 10.1109/tpami.2004.1261097 – ident: ref30 doi: 10.1016/j.eswa.2020.114350 – ident: ref17 doi: 10.1109/tsp.2008.2008254 – ident: ref8 doi: 10.1016/j.compeleceng.2022.107684 – ident: ref21 doi: 10.1023/a:1026065325419 – ident: ref11 doi: 10.1109/tnn.2006.873281 – ident: ref10 doi: 10.1145/1015330.1015408 – ident: ref13 doi: 10.1155/2018/9385947 – ident: ref24 doi: 10.3390/data4010014 – ident: ref2 doi: 10.1016/j.patrec.2004.01.011 – ident: ref3 doi: 10.1007/s521-001-8051-z – ident: ref27 doi: 10.1109/tim.2023.3265114 – ident: ref19 doi: 10.1109/tsp.2020.3001906 – ident: ref12 doi: 10.1109/embc.2013.6611107 – ident: ref4 doi: 10.3390/e21040376 – ident: ref23 doi: 10.1007/s10107-012-0584-1 – ident: ref16 doi: 10.1145/1553374.1553501 – ident: ref28 doi: 10.1109/tnnls.2022.3159573 – ident: ref29 doi: 10.1080/00949655.2017.1327588 – ident: ref15 doi: 10.1561/2200000058 – ident: ref5 doi: 10.1016/j.procs.2019.01.008 – ident: ref14 doi: 10.1561/2200000016 – year: 2019 ident: ref18 article-title: Distributed stochastic nonsmooth nonconvex optimization – ident: ref22 doi: 10.23919/eusipco47968.2020.9287358 – ident: ref31 doi: 10.1007/s00357-019-09349-x |
| SSID | ssj0014496 |
| Score | 2.4437392 |
| Snippet | It is worth noting that the traditional methods for performing both the dimensional reduction and the classification are via the two steps iterative... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1348 |
| SubjectTerms | Algorithms Arithmetic Classification Classification algorithms Clusters EEG Exact solutions Joint PCA and supervised k means algorithm Mathematical analysis Matrices (mathematics) mental arithmetic classification non-iterative analytic optimization Optimization Principal component analysis Principal components analysis Reduction Signal processing algorithms singular value decomposition Symmetric matrices symmetric matrix Transforms |
| Title | Joint Principal Component Analysis and Supervised k Means Algorithm via Non-Iterative Analytic Optimization Approach |
| URI | https://ieeexplore.ieee.org/document/10436436 https://www.proquest.com/docview/2969052277 |
| Volume | 72 |
| WOSCitedRecordID | wos001188290200007&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 Xplore customDbUrl: eissn: 1941-0476 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014496 issn: 1053-587X databaseCode: RIE dateStart: 19910101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZoxQADzyIKBXlgYUhpEye2xwqBAIlSqUXqFjl-QARNqjbt7-fsJKgIMbBlsB3rLvfOfYfQFdPUuNJgAsbBI9L0PWFBPw3jFrBKES6kGzZBh0M2nfJR1azuemG01u7nM921j66Wr3K5sqkykHASgAWNGqhBKS2btb5LBoS4YVzgLwReyOi0rkn2-M1kPIJI0CfdIIhCTsgPG-SGqvzSxM683O__82IHaK_yI_GgZPwh2tLZEdrdQBc8RsVTnmYFHpXpdFhsZT_P4CRcQ5FgkSk8Xs2twlhqhT_wswbbhQefb_kiLd5neJ0KPMwz79GhL4NqLPfCW_ELaJtZ1caJBxU2eQu93t9Nbh-8asiCJ30SFp5KuIGgLGEyIRrcoVAwrXk_MSIJDJF21ps2QkUkkgSIHnIuJaMBV1wpJnwTnKBmBnc_RRgYIEIR-jQ0AQHHhGlBOFMRlZGKtPLb6LomezwvsTRiF4P0eAwsii2L4opFbdSyZN5YV1K4jTo1o-JK2paxzyHGB0eS0rM_tp2jHXt6mTvpoGaxWOkLtC3XRbpcXLoP6QtZyMf_ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB61FKlwaIGC2JaCD1w4hLLJOLaPq6oIWlhWYpH2Fjl-0FUhQbtZfj9jJ4tAVQ-95WDH1kzmnfkG4FA64WNpsCTjkKDx_UQH0E8vVQCssqi0icMmxHAoJxM16prVYy-Mcy7-fOaOw2Os5dvaLEKqjCQcM7Kg-Vt4xxHTftuu9Vw0QIzjuMhjyBIuxWRZlTxR38bXI4oFUzzOspwrxFdWKI5V-UsXRwNz-vE_r7YBHzpPkg1a1m_CG1dtwfoLfMFP0Pysp1XDRm1CnRYH6a8rehNbgpEwXVl2vXgIKmPuLPvDLh1ZLza4u61n0-b3PXucajasq-Q84i-Tcmz30qnsivTNfdfIyQYdOvk23Jz-GH8_S7oxC4lJkTeJLZWnsKyUpkRHDhHX0jnVL70uM48mTHtzXtscc4NCCK6UMVJkyiprpU59tgMrFd19FxgxQHPNU8F9huSaSKdRSZsLk9vc2bQHR0uyFw8tmkYRo5ATVRCLisCiomNRD7YDmV-sayncg70lo4pO3uZFqijKJ1dSiM__2HYA78_GlxfFxfnw1xdYCye1mZQ9WGlmC_cVVs1jM53P9uNH9QTo98tG |
| 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=Joint+Principal+Component+Analysis+and+Supervised+k+Means+Algorithm+via+Non-Iterative+Analytic+Optimization+Approach&rft.jtitle=IEEE+transactions+on+signal+processing&rft.au=Zhang%2C+Zhanbin&rft.au=Ling%2C+Bingo+Wing-Kuen&rft.au=Huang%2C+Guoheng&rft.date=2024&rft.issn=1053-587X&rft.eissn=1941-0476&rft.volume=72&rft.spage=1348&rft.epage=1360&rft_id=info:doi/10.1109%2FTSP.2024.3365944&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TSP_2024_3365944 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-587X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-587X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-587X&client=summon |