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Author Jun Yan ♦ Ning Liu ♦ Benyu Zhang ♦ Qiang Yang ♦ Shuicheng Yan ♦ Zheng Chen
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Linear discriminant analysis ♦ Principal component analysis ♦ Machine learning algorithms ♦ Machine learning ♦ Clustering algorithms ♦ Computational complexity ♦ Asia ♦ Computer science ♦ Statistical distributions ♦ Classification algorithms
Abstract Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
Description Author affiliation: Microsoft Res. Asia, Beijing (Jun Yan; Ning Liu; Benyu Zhang)
ISBN 0769527017
ISSN 15504786
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-12-18
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 203.06 kB
Page Count 10
Starting Page 721
Ending Page 730

Source: IEEE Xplore Digital Library