Sparse Principal Component Analysis via Rotation and Truncation

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 4; pp. 875 - 890
• Main Authors: Hu, Zhenfang, Pan, Gang, Wang, Yueming, Wu, Zhaohui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation algorithms; Approximation methods; Complexity theory; Learning systems; Loading; Principal component analysis; Principal component analysis (PCA); Principal components analysis; rotation; sparse; Sparse matrices; Sparsity; truncation; sparse; truncation; Principal component analysis (PCA); rotation

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 variou...