DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, S | - |
dc.contributor.author | Ahn, JH | - |
dc.contributor.author | Cichocki, A | - |
dc.date.accessioned | 2016-04-01T01:51:01Z | - |
dc.date.available | 2016-04-01T01:51:01Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2006-08 | - |
dc.identifier.issn | 1370-4621 | - |
dc.identifier.other | 2006-OAK-0000006199 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23842 | - |
dc.description.abstract | In this paper, we introduce a new error measure, integrated reconstruction error (IRE) and show that the minimization of IRE leads to principal eigenvectors (without rotational ambiguity) of the data covariance matrix. Then, we present iterative algorithms for the IRE minimization, where we use the projection approximation. The proposed algorithm is referred to as COnstrained Projection Approximation (COPA) algorithm and its limiting case is called COPAL. Numerical experiments demonstrate that these algorithms successfully find exact principal eigenvectors of the data covariance matrix. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | NEURAL PROCESSING LETTERS | - |
dc.subject | natural power iteration | - |
dc.subject | principal component analysis | - |
dc.subject | projection approximation | - |
dc.subject | reconstruction error | - |
dc.subject | subspace analysis | - |
dc.subject | SUBSPACE TRACKING | - |
dc.subject | NEURAL NETWORKS | - |
dc.title | Constrained projection approximation algorithms for principal component analysis | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1007/S11063-006-9 | - |
dc.author.google | Choi, S | - |
dc.author.google | Ahn, JH | - |
dc.author.google | Cichocki, A | - |
dc.relation.volume | 24 | - |
dc.relation.issue | 1 | - |
dc.relation.startpage | 53 | - |
dc.relation.lastpage | 65 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | NEURAL PROCESSING LETTERS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | NEURAL PROCESSING LETTERS, v.24, no.1, pp.53 - 65 | - |
dc.identifier.wosid | 000240310500004 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 65 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 53 | - |
dc.citation.title | NEURAL PROCESSING LETTERS | - |
dc.citation.volume | 24 | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 3 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | natural power iteration | - |
dc.subject.keywordAuthor | principal component analysis | - |
dc.subject.keywordAuthor | projection approximation | - |
dc.subject.keywordAuthor | reconstruction error | - |
dc.subject.keywordAuthor | subspace analysis | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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