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
Online-Zugang:	Volltext
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