---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matthew
      foaf_name: Blaschko, Matthew
      foaf_surname: Blaschko
  - foaf_Person:
      foaf_givenName: Jacquelyn
      foaf_name: Shelton, Jacquelyn
      foaf_surname: Shelton
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Bartels, Andreas
      foaf_surname: Bartels
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Arthur
      foaf_name: Gretton, Arthur
      foaf_surname: Gretton
  bibo_doi: 10.1016/j.patrec.2011.02.011
  bibo_issue: '11'
  bibo_volume: 32
  dct_date: 2011^xs_gYear
  dct_identifier:
  - UT:000293050700010
  dct_language: eng
  dct_publisher: Elsevier@
  dct_title: Semi supervised kernel canonical correlation analysis with application
    to human fMRI@
...