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Robust Manifold Learning Based Ordinal Discriminative Correlation Regression

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11068))

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Abstract

Canonical correlation analysis (CCA) is a typical learning paradigm of capturing the correlation components across multi-views of the same data. When countered with such data with ordinal labels, the accuracy performance yielded by traditional CCA is usually not desirable because of ignoring the ordinal relationships among data labels. In order to incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA (OR-DisCCA) was presented. Although OR-DisCCA can yield better ordinal regression results, its performance will be deteriorated when the data are corrupted with outliers because the ordered class centers easily tend to be biased by the outliers. To address this issue, in this work we construct robust manifold ordinal discriminative correlation regression (rmODCR) by replacing the traditional (\(l_2\)-norm) class centers with \(l_p\)-norm centers in objective optimization. Finally, we experimentally evaluate the effectiveness of the proposed method.

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Acknowledgment

This work was partially supported by the National Natural Science Foundation of China under grant 61702273, the Natural Science Foundation of Jiangsu Province under grant BK20170956, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant 17KJB520022, a Project Funded by the Priority Academic Program Development of Jiangsu Higer Education Institutions, and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.

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Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Qing Tian & Wenqiang Zhang

  2. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Qing Tian & Wenqiang Zhang

  3. Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Liping Wang

  4. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Qing Tian

Authors
  1. Qing Tian
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  2. Wenqiang Zhang
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  3. Liping Wang
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Corresponding author

Correspondence to Qing Tian .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Tian, Q., Zhang, W., Wang, L. (2018). Robust Manifold Learning Based Ordinal Discriminative Correlation Regression. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_60

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  • DOI: https://doi.org/10.1007/978-3-030-00021-9_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00020-2

  • Online ISBN: 978-3-030-00021-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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