A New DC Algorithm for Sparse Optimal Scoring Problem

Linear discriminant analysis (LDA) has attracted many attentions as a classical tool for both classification and dimensionality reduction. Classical LDA performs quite well in simple and low dimensional setting while it is not suitable for small sample size data (SSS). Feature selection is an effect...

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Veröffentlicht in:IEEE access Jg. 8; S. 53962 - 53971
Hauptverfasser: Li, Guo-Quan, Duan, Xu-Xiang, Wu, Chang-Zhi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Continuity (mathematics)
Convergence
Convex functions
dc algorithm
Dimensionality reduction
Discriminant analysis
Exact solutions
Feature extraction
Linear discriminant analysis
Optimization
Regularization
sparse optimal scoring
ℓ₀-norm
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Linear discriminant analysis (LDA) has attracted many attentions as a classical tool for both classification and dimensionality reduction. Classical LDA performs quite well in simple and low dimensional setting while it is not suitable for small sample size data (SSS). Feature selection is an effective way to solve this problem. As a variant of LDA, sparse optimal scoring (SOS) with 10-norm regularization is considered in this paper. By using a new continuous nonconvex nonsmooth function to approximate 10-norm, we propose a novel difference of convex functions algorithm (DCA) for sparse optimal scoring. The most favorable property of the proposed DCA is its subproblem admits an analytical solution. The effectiveness of the proposed method is validated via theoretical analysis as well as some illustrative numerical experiments.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2981429