NDLI: A Comparison of Unsupervised Dimension Reduction Algorithms for Classification

Content Provider	IEEE Xplore Digital Library
Author	Jaegul Choo Hyunsoo Kim Haesun Park Hongyuan Zha
Copyright Year	2007
Description	Author affiliation: Georgia Inst. of Technol., Atlanta (Jaegul Choo; Hyunsoo Kim; Haesun Park; Hongyuan Zha)
Abstract	Distance preserving dimension reduction (DPDR) using the singular value decomposition has recently been introduced. In this paper, for disease diagnosis using gene or protein expression data, we present empirical comparison results between DPDR and other various dimension reduction (DR) methods (i.e. PC A, MDS, Isomap, and LLE) when using support vector machines with radial basis function kernel. Our results show that DPDR outperforms, as a whole, other DR methods in terms of classification accuracy, but at the same time, it gives significant efficiency compared with other methods since it has no parameter to be optimized. Based on these empirical results, we reach a promising conclusion that DPDR is one of the best DR methods at hand for modeling an efficient and distortion- free classifier for gene or protein expression data.
Starting Page	71
Ending Page	77
File Size	160862
Page Count	7
File Format	PDF
ISBN	9780769530314
DOI	10.1109/BIBM.2007.51
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machines Support vector machine classification Feature extraction Classification algorithms Bioinformatics Kernel Principal component analysis Diseases Singular value decomposition
Content Type	Text
Resource Type	Article

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