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Two-dimensional locality adaptive discriminant analysis

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Abstract

Two-dimensional Linear Discriminant Analysis (2DLDA), which is supervised and extracts the most discriminating features, has been widely used in face image representation and recognition. However, 2DLDA is inapplicable to many real-world situations because it assumes that the input data obeys the Gaussian distribution and emphasizes the global relationship of data merely. To handle this problem, we present a Two-dimensional Locality Adaptive Discriminant Analysis (2DLADA). Compared to 2DLDA, our method has two salient advantages: (1) it does not depend on any assumptions on the data distribution and is more suitable in real world applications; (2) it adaptively exploits the intrinsic local structure of data manifold. Performance on artificial dataset and real-world datasets demonstrate the superiority of our proposed method.

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Acknowledgments

This work is supported by the Collaborative Innovation Platform of shenzhen institute of information technology, theShenzhen Fundamental Research fund under Grant JCYJ20160530141902978, the National Natural Science Foundation of China under Grant 61773302, and the Natural Science Foundation of Ningbo: 2018A610049. We thank Yinfeng Wang and Xiaojun Yang for their language editing, thank Deyan Xie for her writing assistance.

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Authors and Affiliations

  1. School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, China

    Qin Li

  2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Jane You

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  1. Qin Li
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  2. Jane You
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Correspondence to Qin Li.

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Li, Q., You, J. Two-dimensional locality adaptive discriminant analysis. Multimed Tools Appl 78, 30397–30418 (2019). https://doi.org/10.1007/s11042-019-07861-1

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  • DOI: https://doi.org/10.1007/s11042-019-07861-1

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