Sparse unsupervised dimensionality reduction algorithms

Authors:
Wenjun Dou;Guang Dai;Congfu Xu;Zhihua Zhang
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Year:
2010

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Abstract

Principal component analysis (PCA) and its dual--principal coordinate analysis (PCO)--are widely applied to unsupervised dimensionality reduction. In this paper, we show that PCAand PCOcan be carried out under regression frameworks. Thus, it is convenient to incorporate sparse techniques into the regression frameworks. In particular, we propose a sparse PCA model and a sparse PCO model. The former is to find sparse principal components, while the latter directly calculates sparse principal coordinates in a low-dimensional space. Our models can be solved by simple and efficient iterative procedures. Finally, we discuss the relationship of our models with other existing sparse PCA methods and illustrate empirical comparisons for these sparse unsupervised dimensionality reduction methods. The experimental results are encouraging.