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  Semi-supervised Analysis of Human fMRI Data

Shelton, J., Blaschko, M., Lampert, C., & Bartels, A. (2009). Semi-supervised Analysis of Human fMRI Data. Poster presented at Berlin BCI Workshop 2009: Advances in Nanotechnology, Berlin, Germany.

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 Creators:
Shelton, JA1, 2, Author           
Blaschko, MB, Author           
Lampert, CH1, 2, Author           
Bartels, A2, 3, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 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, CCA 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, CCA may suffer from small sample effects. We propose to use semisupervised
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 CCA 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,
the semi-supervised variants of CCA 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.

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 Dates: 2009-07
 Publication Status: Issued
 Pages: -
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 Identifiers: BibTex Citekey: 5935
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Title: Berlin BCI Workshop 2009: Advances in Nanotechnology
Place of Event: Berlin, Germany
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