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Two-dimensional discriminant analysis based on Schatten p-norm for image feature extraction.
- Source :
-
Journal of Visual Communication & Image Representation . May2017, Vol. 45, p87-94. 8p. - Publication Year :
- 2017
-
Abstract
- A Schatten p -norm-based two-dimensional principal component analysis (2DPCA-SP) method was proposed for image feature extraction in our previous work. As an unsupervised method, 2DPCA-SP ignores the label information of training samples, which is essential to classification tasks. In this paper, we propose a novel Schatten p -norm-based two-dimensional discriminant analysis (2DDA-SP) method for image feature extraction, which learns an optimal projection matrix by maximizing the difference of Schatten p -norm-based between-class dispersion and Schatten p -norm-based within-class dispersion in low-dimensional feature space. By using both the Schatten p -norm metric and the label information of training samples, 2DDA-SP not only can efficiently extract discriminative features, but is also robust to outliers. We also propose an efficient iterative algorithm to solve the optimization problem of 2DDA-SP with 0 < p < 1 . Experimental results on several image databases show that 2DDA-SP with 0 < p < 1 is effective and robust for image feature extraction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 45
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 121939016
- Full Text :
- https://doi.org/10.1016/j.jvcir.2017.02.015