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PKDD
2010
Springer

Sparse Unsupervised Dimensionality Reduction Algorithms

10 years 11 months ago
Sparse Unsupervised Dimensionality Reduction Algorithms
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 PCA and PCO can 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.
Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PKDD
Authors Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang
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