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Simple and Robust Locality Preserving Projections Based on Maximum Difference Criterion

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Abstract

The locality preserving projections (LPP) method is a hot dimensionality reduction method in the machine learning field. But the LPP method has the so-called small-sample-size problem, and its performance is unstable when the neighborhood size parameter k varies. In this paper, by theoretical analysis and derivation, a maximum difference criterion for the LPP method is constructed, and then a simple and robust LPP method has been proposed, called Locality Preserving Projections based on the approximate maximum difference criterion (LPPMDC). Compared with the existing approaches to solve the small-sample-size problem of LPP, the proposed LPPMDC method has three superiorities: (1) it has no the small-sample-size problem and can get the better performance, (2) it is robust to neighborhood size parameter k, (3) it has low computation complexity. The experiments are performed on the three face databases: ORL, Georgia Tech, and FERET, and the results demonstrate that LPPMDC is an efficient and robust method.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under grant 61876026, Humanities and Social Sciences Project of the Ministry of Education of China under grant 20YJAZH084, Chongqing Technology Innovation and Application Development Project under Grant cstc2020jscx-msxmX0190 and cstc2019jscx-mbdxX0061, Project of Natural Science Foundation Project of CQ CSTC of China under grant cstc2016jcyjA0419 and cstc2017jcyjAX0316, and Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJZD-K202100505.

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Authors and Affiliations

  1. College of Computer and Information Science, College of Intelligent Science, Chongqing Normal University, Chongqing, 401331, China

    Ruisheng Ran & Hao Qin

  2. Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application, Chongqing, 401331, China

    Ruisheng Ran & Hao Qin

  3. School of Mathematical Sciences, Chongqing Normal University, Chongqing, 401331, China

    Shougui Zhang

  4. College of computer science, Chongqing University, Chongqing, 400044, China

    Bin Fang

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  1. Ruisheng Ran
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  2. Hao Qin
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  3. Shougui Zhang
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  4. Bin Fang
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Correspondence to Ruisheng Ran.

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Ran, R., Qin, H., Zhang, S. et al. Simple and Robust Locality Preserving Projections Based on Maximum Difference Criterion. Neural Process Lett 54, 1783–1804 (2022). https://doi.org/10.1007/s11063-021-10706-4

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