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キーワード:
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要旨:
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 tied more closely
to underlying process generating the the data and can ignore high-variance noise directions. However,
for data where acquisition in a given modality 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 manifold learning 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 and such 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 multivariate labels (corresponding to video content subjects viewed during
the image acquisition). In each variate condition, Laplacian regularization improved performance whereas
the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights
learned by the regression in order to infer brain regions that are important during different types of visual processing.
