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Joint of locality- and globality-preserving projections

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Abstract

Dimensionality reduction is an important topic in machine learning community, which is widely used in the areas of face recognition, visual detection and tracking. Preserving local and global structures simultaneously is crucial for dimensionality reduction. In this paper, local and global approaches are generalized, respectively, and then a unified framework that joins the effective local and global terms is presented for unsupervised dimensionality reduction. Furthermore, to search for the optimal integration parameter, the proposed method uses two different search schemes named JLGP and IJLGP, respectively, where JLGP corresponds to the manual search scheme and IJLGP corresponds to the automatic search schemes. The promising experimental results on four benchmark datasets validate the effectiveness of the proposed method.

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Acknowledgements

This study was supported by the Shenzhen Research Council (Grant Nos. JCYJ20170413104556946, JCYJ20160406161948211, JCYJ20160226201453085, JSGG20150331152017052), by the National Natural Science Foundation of China (Grant Nos. 61672183, 61272252, U1509216, 61472099), by Science and Technology Planning Project of Guangdong Province (Grant No. 2016B090918047) and by Natural Science Foundation of Guangdong Province (Grant No. 2015A030313544).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Xiaohuan Lu, Zhenyu He & Shuangyan Yi

  2. College of Mathematics and Statistics, Shenzhen Key Laboratory of Media Security, Shenzhen Universiy, Shenzhen, China

    Wen-Sheng Chen

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  1. Xiaohuan Lu
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  2. Zhenyu He
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  3. Shuangyan Yi
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  4. Wen-Sheng Chen
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Correspondence to Zhenyu He.

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Lu, X., He, Z., Yi, S. et al. Joint of locality- and globality-preserving projections. SIViP 12, 565–572 (2018). https://doi.org/10.1007/s11760-017-1194-4

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