Robust sparse principal component analysis

• Published in: Science China. Information sciences Vol. 57; no. 9; pp. 171 - 184
• Main Authors: Zhao, Qian, Meng, DeYu, Xu, ZongBen
• Format: Journal Article
• Language: English
• Published: Heidelberg Science China Press 01.09.2014; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Information Systems and Communication Service; L1范数; L2范数; Noise sensitivity; Outliers (statistics); P-范数; PCA方法; PCA模型; Principal components analysis; Research Paper; Robustness; 主成分分析; 健壮; 稀疏; noise; outlier; robustness; sparsity; principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsi...