Sparse Principal Component Analysis via Fractional Function Regularity

Xuanli Han, Jigen Peng, Angang Cui and Fujun Zhao

Mathematical Problems in Engineering, 2020, vol. 2020, 1-10

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7874140

DOI: 10.1155/2020/7874140

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