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Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction

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Abstract

Two-dimensional locality-preserving projection (2DLPP) is an unsupervised method, so it can’t use the discrimination information of the sample in the sparse data; elastic net regression can obtain a sparse results of the feature extraction. So, this paper presents a new method for image feature extraction, namely the sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) based on the 2D discriminant locality-preserving projection (2DDLPP) and elastic net regression. By adding the between-class scatter and discrimination information into the objective function of 2DLPP, S2DDLPP uses elastic net regression to obtain an optimal sparse projection matrix with “minimizing the within-class scatter” and “maximizing the between-class scatter.” Compared with other methods (2DPCA, 2DPCA-L1, 2DLDA, 2DLPP, 2DDLPP, and 2DDLPP-L1), the experimental results on the ORL, Yale, AR and FERET face database show the effectiveness of the proposed algorithm.

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Acknowledgements

This work is partially supported by National Key R&D Program Grant No. 2017YFC0804002, the National Science Foundation of China under Grant Nos. 61462064, 6177227, 61362031, 61463008, 61403188, 61503195, 61603192, and the China Postdoctoral Science Foundation under Grant No. 2016M600674, the Natural Science Fund of Jiangsu Province under Grants BK20161580, BK20171494 and China’s Aviation Science (No. 20145556011).

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Correspondence to Minghua Wan.

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Wan, M., Yang, G., Sun, C. et al. Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction. Soft Comput 23, 5511–5518 (2019). https://doi.org/10.1007/s00500-018-3207-9

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