Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to
improve the interpretability over the dense basis of PCA, while still covering the data
subspace as much as possible. In contrast to most existing work that addresses the problem
by adding sparsity penalties on various objectives of PCA, we propose a new method,
sparse PCA via rotation and truncation (SPCArt), which finds a rotation matrix and a sparse
basis such that the sparse basis approximates the basis of PCA after the rotation. The …

This chapter begins with the motivation of sparse PCA–to improve the physical interpretation
of the loadings. Second, we introduce the issues involved in sparse PCA problem that are
distinct from PCA problem. Third, we briefly review some sparse PCA algorithms in the
literature, and comment their limitations as well as problems unresolved. Forth, we introduce
one of the state-of-the-art algorithms, SPCArt Hu et al.(IEEE Trans. Neural Networks Learn.
Syst. 27 (4): 875–890, 2016), including its motivating idea, formulation, optimization solution …