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Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing

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Abstract

With the rapid growth of high dimensional data, dimensionality reduction is playing a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pairwise constraints. In this setting, domain knowledge is given in the form of pairwise constraints, which specifies whether a pair of instances belong to the same class (must-link constraint) or different classes (cannot-link constraint). In this paper, a novel semi-supervised dimensionality reduction method called adaptive semi-supervised dimensionality reduction (ASSDR) is proposed, which can get the optimized low dimensional representation of the original data by adaptively adjusting the weights of the pairwise constraints and simultaneously optimizing the graph construction. Experiments on UCI classification and image recognition show that ASSDR is superior to many existing dimensionality reduction methods.

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Notes

  1. http://archive.ics.uci.edu/ml/.

  2. http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html.

  3. http://www4.comp.polyu.edu.hk/~biometrics/.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61402181, 61273363), the Science and Technology Programme of Guangzhou Municipal Government (2014J4100006), the Guangdong Natural Science Foundation (S2012040008022, S2012010009961).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Meng Meng, Jia Wei, Jiabing Wang & Qianli Ma

  2. Computer Application Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Xuan Wang

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  1. Meng Meng
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  2. Jia Wei
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Correspondence to Jia Wei.

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Meng, M., Wei, J., Wang, J. et al. Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing. Int. J. Mach. Learn. & Cyber. 8, 793–805 (2017). https://doi.org/10.1007/s13042-015-0380-3

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