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Sparsity Regularization Discriminant Projection for Feature Extraction

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Abstract

Recently, sparse representation models have attracted considerable interests in the field of feature extraction. In this paper, we propose a novel supervised feature extraction method called sparsity regularization discriminant projection (SRDP), which aims to preserve the sparse representation structure of the data and simultaneously maximize the ratio of nonlocal scatter to local scatter. More specifically, SRDP first constructs a concatenated dictionary through the class-wise principal component analysis decompositions. Second, the sparse representation structure of each sample is quickly learned with the constructed dictionary by matrix–vector multiplications. Then SRDP regards the learned sparse representation structure as an additional regularization term of unsupervised discriminant projection so as to construct a new discriminant function. Finally, SRDP is transformed into a generalized eigenvalue problem. Experimental results on five representative image databases demonstrate the effectiveness of our proposed method.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable and constructive criticisms that are very helpful to improve the quality of this paper. This work was supported by the National Science Foundation of China (Grant No. 61603013).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Sen Yuan, Xia Mao & Lijiang Chen

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  1. Sen Yuan
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  2. Xia Mao
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  3. Lijiang Chen
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Correspondence to Lijiang Chen.

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Yuan, S., Mao, X. & Chen, L. Sparsity Regularization Discriminant Projection for Feature Extraction. Neural Process Lett 49, 539–553 (2019). https://doi.org/10.1007/s11063-018-9842-4

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