Release Time:2019-03-11  Hits:

Indexed by: Journal Article

Date of Publication: 2017-09-01

Journal: NEURAL NETWORKS

Included Journals: PubMed、EI、SCIE、Scopus

Volume: 93

Page Number: 205-218

ISSN: 0893-6080

Key Words: Linear discriminant analysis; Feature extraction; Absolute value; Robust modeling; Sparse projection

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. (C) 2017 Elsevier Ltd. All rights reserved.