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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 432–446Cite as

Regularization and Shrinkage in Rough Set Based Canonical Correlation Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Abstract

The modern technology has enabled very high dimensional multimodal data streams to be routinely acquired, which results in very high dimensional feature spaces () as compared to number of training samples (n). In this regard, the paper presents a new feature extraction algorithm to address the ‘small n and large ’ problem associated with multimodal data sets. It judiciously integrates both regularization and shrinkage with canonical correlation analysis (CCA). While the diagonal elements of covariance matrices are increased using regularization parameters, the off-diagonal elements are decreased by shrinkage parameters. The theory of rough sets is used to find out the optimum regularization parameters of CCA. The effectiveness of the proposed method, along with a comparison with other methods, is demonstrated on three pairs of modalities of two real life data sets.

This work is partially supported by the Department of Electronics and Information Technology, Government of India (PhD-MLA/4(90)/2015-16).

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Author information

Authors and Affiliations

  1. Biomedical Imaging and Bioinformatics Lab Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Ankita Mandal & Pradipta Maji

Authors
  1. Ankita Mandal
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  2. Pradipta Maji
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Corresponding author

Correspondence to Pradipta Maji .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Mandal, A., Maji, P. (2017). Regularization and Shrinkage in Rough Set Based Canonical Correlation Analysis. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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