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Matrix Entropy Driven Maximum Margin Feature Learning

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PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11012))

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Abstract

This paper proposes an efficient supervised Matrix Entropy Driven Maximum Margin Feature Learning method (M3FL) to optimize all the discriminative features simultaneously. Specifically, we first present an in-depth investigation to the heteroscedastic problem in the maximum margin criterion, and then propose a new Maximum Margin Framework (MMF) based on the analysis to improve the traditional maximum margin criterion. The proposed MMF is robust to the initialization by exploring the \(\ell _1\)-norm property. We further analyze the proposed MMF and find that it is necessary to learn the projection matrix from the perspective of matrix entropy. Consequently, the M3FL method is proposed to make the matrix entropy of the projection matrix as small as possible, and the corresponding optimization algorithm is developed. In addition, we discuss the relationship between the proposed optimization algorithm w.r.t. M3FL and the optimization algorithm w.r.t. MMF. Experiments are conducted on six widely-used data sets and experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Dong Zhang, Jinhui Tang & Zechao Li

Authors
  1. Dong Zhang
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  2. Jinhui Tang
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  3. Zechao Li
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Corresponding author

Correspondence to Jinhui Tang .

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Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

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Zhang, D., Tang, J., Li, Z. (2018). Matrix Entropy Driven Maximum Margin Feature Learning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-97304-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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