Sparse Unsupervised Dimensionality Reduction Algorithms

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.