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Matrix-Variate Discriminative Analysis, Integrative Hypothesis Testing, and Geno-Pheno A5 Analyzer

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Intelligent Science and Intelligent Data Engineering (IScIDE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7751))

Abstract

A general perspective is provided on both on hypothesis testing and discriminative analyses, by which matrix-variate discriminative analyses are proposed based on the matrix normal distribution, featured by a bi-linear extension of Fisher linear discriminant analysis and a further extension to binary variables. Moreover, a general formulation is proposed for integrative hypothesis testing and five typical categories are summarized. Furthermore, major techniques for variable selection are briefly elaborated. Finally, taking analyses of gene expression and exome sequencing as examples, we further propose a general procedure called Geno-Pheno A5 Analyzer for integrative discriminant analysis.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Chinese Univ. of Hong Kong, Hong Kong

    Lei Xu

  2. Chang Jiang Chair Professor Program, School of EE&CS, Peking Univ., Beijing, China

    Lei Xu

Authors
  1. Lei Xu
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Editor information

Editors and Affiliations

  1. Nanjing University of Science & Technology, 210094, Nanjing, China

    Jian Yang

  2. Department of Psychology, Peking University, 100871, Beijing, China

    Fang Fang

  3. School of Automation, Southeast University, 210096, Nanjing, P.R. China

    Changyin Sun

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Xu, L. (2013). Matrix-Variate Discriminative Analysis, Integrative Hypothesis Testing, and Geno-Pheno A5 Analyzer. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_105

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  • DOI: https://doi.org/10.1007/978-3-642-36669-7_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36668-0

  • Online ISBN: 978-3-642-36669-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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