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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Vydané v:	IEEE access Ročník 8; s. 53962 - 53971
Hlavní autori:	Li, Guo-Quan, Duan, Xu-Xiang, Wu, Chang-Zhi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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