Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers

In this paper, we propose a robust linear discriminant analysis (RLDA) through Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L 1 -norm operation that makes it less sensitive to outliers and noise than the L 2 -norm linear discriminant analysis (LDA). In addition...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2020-03, Vol.31 (3), p.915-926
Hauptverfasser:	Li, Chun-Na, Shao, Yuan-Hai, Yin, Wotao, Liu, Ming-Zeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Alternating direction method of multipliers (ADMMs) Covariance matrices Data analysis dimensionality reduction (DR) Discriminant analysis Error analysis Face Feature extraction Formulations Learning systems Linear discriminant analysis linear discriminant analysis (LDA) Multipliers Noise Noise sensitivity Optimization Outliers (statistics) robust linear discriminant analysis (RLDA) Robustness sparse feature extraction
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