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- Title
Sparse Principal Component Analysis via Fractional Function Regularity.
- Authors
Han, Xuanli; Peng, Jigen; Cui, Angang; Zhao, Fujun
- Abstract
In this paper, we describe a novel approach to sparse principal component analysis (SPCA) via a nonconvex sparsity-inducing fraction penalty function SPCA (FP-SPCA). Firstly, SPCA is reformulated as a fraction penalty regression problem model. Secondly, an algorithm corresponding to the model is proposed and the convergence of the algorithm is guaranteed. Finally, numerical experiments were carried out on a synthetic data set, and the experimental results show that the FP-SPCA method is more adaptable and has a better performance in the tradeoff between sparsity and explainable variance than SPCA.
- Subjects
PRINCIPAL components analysis; ALGORITHMS; REGRESSION analysis
- Publication
Mathematical Problems in Engineering, 2020, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2020/7874140