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Functional Multiple-Set Canonical Correlation Analysis

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Abstract

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the method solves a matrix eigen-analysis problem through the adoption of a basis expansion approach to approximating data and weight functions. We apply the proposed method to functional magnetic resonance imaging (fMRI) data to identify networks of neural activity that are commonly activated across subjects while carrying out a working memory task.

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Authors and Affiliations

  1. Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada

    Heungsun Hwang

  2. McGill University, Montreal, QC, Canada

    Kwanghee Jung & Yoshio Takane

  3. University of British Columbia and British Columbia Mental Health and Addiction Research Institute, Vancouver, BC, Canada

    Todd S. Woodward

Authors
  1. Heungsun Hwang
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  2. Kwanghee Jung
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  3. Yoshio Takane
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  4. Todd S. Woodward
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Correspondence to Heungsun Hwang.

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Hwang, H., Jung, K., Takane, Y. et al. Functional Multiple-Set Canonical Correlation Analysis. Psychometrika 77, 48–64 (2012). https://doi.org/10.1007/s11336-011-9234-4

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  • DOI: https://doi.org/10.1007/s11336-011-9234-4

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