---
_id: '3389'
abstract:
- lang: eng
  text: Kernel canonical correlation analysis (KCCA) is a general technique for subspace
    learning that incorporates principal components analysis (PCA) and Fisher linear
    discriminant analysis (LDA) as special cases. By finding directions that maximize
    correlation, KCCA learns representations that are more closely tied to the underlying
    process that generates the data and can ignore high-variance noise directions.
    However, for data where acquisition in one or more modalities is expensive or
    otherwise limited, KCCA may suffer from small sample effects. We propose to use
    semi-supervised Laplacian regularization to utilize data that are present in only
    one modality. This approach is able to find highly correlated directions that
    also lie along the data manifold, resulting in a more robust estimate of correlated
    subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally
    amenable to subspace techniques as data are well aligned. fMRI data of the human
    brain are a particularly interesting candidate. In this study we implemented various
    supervised and semi-supervised versions of KCCA on human fMRI data, with regression
    to single and multi-variate labels (corresponding to video content subjects viewed
    during the image acquisition). In each variate condition, the semi-supervised
    variants of KCCA performed better than the supervised variants, including a supervised
    variant with Laplacian regularization. We additionally analyze the weights learned
    by the regression in order to infer brain regions that are important to different
    types of visual processing.
acknowledgement: The research leading to these results has received funding from the
  European Research Council under the European Community’s Seventh Framework Programme
  (FP7/2007-2013)/ERC Grant Agreement No. 228180. This work was funded in part by
  the EC project CLASS, IST 027978, and the PASCAL2 network of excellence, IST 2002-506778.
article_processing_charge: No
author:
- first_name: Matthew
  full_name: Blaschko, Matthew
  last_name: Blaschko
- first_name: Jacquelyn
  full_name: Shelton, Jacquelyn
  last_name: Shelton
- first_name: Andreas
  full_name: Bartels, Andreas
  last_name: Bartels
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Arthur
  full_name: Gretton, Arthur
  last_name: Gretton
citation:
  ama: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel
    canonical correlation analysis with application to human fMRI. <i>Pattern Recognition
    Letters</i>. 2011;32(11):1572-1583. doi:<a href="https://doi.org/10.1016/j.patrec.2011.02.011">10.1016/j.patrec.2011.02.011</a>
  apa: Blaschko, M., Shelton, J., Bartels, A., Lampert, C., &#38; Gretton, A. (2011).
    Semi supervised kernel canonical correlation analysis with application to human
    fMRI. <i>Pattern Recognition Letters</i>. Elsevier. <a href="https://doi.org/10.1016/j.patrec.2011.02.011">https://doi.org/10.1016/j.patrec.2011.02.011</a>
  chicago: Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert,
    and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with
    Application to Human FMRI.” <i>Pattern Recognition Letters</i>. Elsevier, 2011.
    <a href="https://doi.org/10.1016/j.patrec.2011.02.011">https://doi.org/10.1016/j.patrec.2011.02.011</a>.
  ieee: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised
    kernel canonical correlation analysis with application to human fMRI,” <i>Pattern
    Recognition Letters</i>, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011.
  ista: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. 2011. Semi supervised
    kernel canonical correlation analysis with application to human fMRI. Pattern
    Recognition Letters. 32(11), 1572–1583.
  mla: Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis
    with Application to Human FMRI.” <i>Pattern Recognition Letters</i>, vol. 32,
    no. 11, Elsevier, 2011, pp. 1572–83, doi:<a href="https://doi.org/10.1016/j.patrec.2011.02.011">10.1016/j.patrec.2011.02.011</a>.
  short: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition
    Letters 32 (2011) 1572–1583.
date_created: 2018-12-11T12:03:03Z
date_published: 2011-08-01T00:00:00Z
date_updated: 2025-09-30T08:45:21Z
day: '01'
department:
- _id: ChLa
doi: 10.1016/j.patrec.2011.02.011
external_id:
  isi:
  - '000293050700010'
intvolume: '        32'
isi: 1
issue: '11'
language:
- iso: eng
month: '08'
oa_version: None
page: 1572 - 1583
publication: Pattern Recognition Letters
publication_status: published
publisher: Elsevier
publist_id: '3218'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Semi supervised kernel canonical correlation analysis with application to human
  fMRI
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 32
year: '2011'
...