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Abstract

Kernel Canonical Correlation Analysis is a very general technique for subspace learning that incorporates
PCA and LDA as special cases. Functional magnetic resonance imaging (fMRI) acquired data is naturally
amenable to these techniques as data are well aligned. fMRI data of the human brain is 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.