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Sparse Principal Component Analysis via Fractional Function Regularity
Joint Authors
Han, Xuanli
Peng, Jigen
Cui, Angang
Zhao, Fujun
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Han, Xuanli& Peng, Jigen& Cui, Angang& Zhao, Fujun. 2020. Sparse Principal Component Analysis via Fractional Function Regularity. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1200747
Modern Language Association (MLA)
Han, Xuanli…[et al.]. Sparse Principal Component Analysis via Fractional Function Regularity. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1200747
American Medical Association (AMA)
Han, Xuanli& Peng, Jigen& Cui, Angang& Zhao, Fujun. Sparse Principal Component Analysis via Fractional Function Regularity. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1200747
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200747