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Robust L1-norm two-dimensional linear discriminant analysis
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DOI：10.1016/j.neunet.2015.01.003.png)
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In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Linear discriminant analysis
Two-dimensional linear discriminant analysis
L1-norm two-dimensional linear discriminant analysis
Dimensionality reduction
Iterative technique
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