Orthogonal sparse linear discriminant analysis |
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Authors: | Zhonghua Liu Gang Liu Jiexin Pu Xiaohong Wang Haijun Wang |
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Affiliation: | 1. Department of Computer, Information Engineering College, Henan University of Science and Technology, Luoyang, China;2. Centre of Quantum Computation and Intelligent Systems, University of Technology Sydney, Sydney, Australia |
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Abstract: | Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm. |
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Keywords: | Linear discriminant analysis (LDA) L2 1-norm nearest neighbour orthogonal sparse linear discriminant analysis (OSLDA) |
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