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dc.contributor.authorNiijima, Satoshija
dc.contributor.authorOkuno, Yasushija
dc.contributor.alternative新島, 聡ja
dc.date.accessioned2010-02-24T02:35:23Z-
dc.date.available2010-02-24T02:35:23Z-
dc.date.issued2009-10-
dc.identifier.issn1557-9964ja
dc.identifier.urihttp://hdl.handle.net/2433/98006-
dc.description.abstractUntil recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis. In this paper, we address the problem of unsupervised feature selection. First, we extend Laplacian linear discriminant analysis (LLDA) to unsupervised cases. Second, we propose a novel algorithm for computing LLDA, which is efficient in the case of high dimensionality and small sample size as in microarray data. Finally, an unsupervised feature selection method, called LLDA-based Recursive Feature Elimination (LLDA-RFE), is proposed. We apply LLDA-RFE to several public data sets of cancer microarrays and compare its performance with those of Laplacian score and SVD-entropy, two state-of-the-art unsupervised methods, and with that of Fisher score, a supervised filter method. Our results demonstrate that LLDA-RFE outperforms Laplacian score and shows favorable performance against SVD-entropy. It performs even better than Fisher score for some of the data sets, despite the fact that LLDA-RFE is fully unsupervised.ja
dc.language.isoengja
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)ja
dc.rightsc 2009 IEEE.ja
dc.subjectGraph Laplacianja
dc.subjectLinear discriminant analysisja
dc.subjectMicroarray data analysisja
dc.subjectUnsupervised feature selectionja
dc.subject.meshAlgorithms-
dc.subject.meshArtificial Intelligence-
dc.subject.meshComputational Biology/methods-
dc.subject.meshDiscriminant Analysis-
dc.subject.meshGene Expression Profiling/methods-
dc.subject.meshGene Expression Regulation, Neoplastic-
dc.subject.meshHumans-
dc.subject.meshModels, Statistical-
dc.subject.meshOligonucleotide Array Sequence Analysis/methods-
dc.subject.meshPattern Recognition, Automated/methods-
dc.subject.meshProgramming Languages-
dc.subject.meshSoftware-
dc.titleLaplacian linear discriminant analysis approach to unsupervised feature selection.ja
dc.type.niitypeJournal Articleja
dc.identifier.jtitleIEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACMja
dc.identifier.volume6ja
dc.identifier.issue4ja
dc.identifier.spage605ja
dc.identifier.epage614ja
dc.relation.doi10.1109/TCBB.2007.70257ja
dc.textversionpublisherja
dc.identifier.pmid19875859-
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