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Your search keyword '"Liu, Ming-Zeng"' showing total 2 results
Start Over Author "Liu, Ming-Zeng" Topic feature extraction
2 results on '"Liu, Ming-Zeng"'

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

Author

Li, Chun-Na, Shao, Yuan-Hai, Yin, Wotao, and Liu, Ming-Zeng

Subjects
FISHER discriminant analysis, DISCRIMINANT analysis, FACE
Abstract

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, we extend our RLDA to a sparse model (RSLDA). Both RLDA and RSLDA can extract unbounded numbers of features and avoid the small sample size (SSS) problem, and an alternating direction method of multipliers (ADMM) is used to cope with the nonconvexity in the proposed formulations. Compared with the traditional LDA, our RLDA and RSLDA are more robust to outliers and noise, and RSLDA can obtain sparse discriminant directions. These findings are supported by experiments on artificial data sets as well as human face databases. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Robust recursive absolute value inequalities discriminant analysis with sparseness.

Author

Li, Chun-Na, Zheng, Zeng-Rong, Liu, Ming-Zeng, Shao, Yuan-Hai, and Chen, Wei-Jie

Subjects
*MATHEMATICAL regularization, *FEATURE extraction, *DISCRIMINANT analysis, *ROBUST control, *PROBLEM solving, *MATHEMATICAL optimization
Abstract

In this paper, we propose a novel absolute value inequalities discriminant analysis (AVIDA) criterion for supervised dimensionality reduction. Compared with the conventional linear discriminant analysis (LDA), the main characteristics of our AVIDA are robustness and sparseness. By reformulating the generalized eigenvalue problem in LDA to a related SVM-type “concave-convex” problem based on absolute value inequalities loss, our AVIDA is not only more robust to outliers and noises, but also avoids the SSS problem. Moreover, the additional L1-norm regularization term in the objective makes sure sparse discriminant vectors are obtained. A successive linear algorithm is employed to solve the proposed optimization problem, where a series of linear programs are solved. The superiority of our AVIDA is supported by experimental results on artificial examples as well as benchmark image databases. [ABSTRACT FROM AUTHOR]

Published
2017
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