NDLI: Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection

Content Provider	IEEE Xplore Digital Library
Author	Niijima, S. Okuno, Y.
Copyright Year	2004
Abstract	Until 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.
Sponsorship	IEEE Computer Society
Page Count	10
File Size	1854466
Starting Page	605
Ending Page	614
File Format	PDF
ISSN	15455963
Volume Number	6
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laplace equations Linear discriminant analysis Genomics Bioinformatics Proteomics Biomarkers Mass spectroscopy Data analysis Cancer Filters microarray data analysis. Unsupervised feature selection linear discriminant analysis graph Laplacian Biology and genetics Feature evaluation and selection Medicine
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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