English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Semi-supervised kernel canonical correlation analysis with application to human fMRI

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. doi:10.1016/j.patrec.2011.02.011.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BAC0-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B20B-A
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Blaschko, MB, Author              
Shelton, JA, Author              
Bartels, A1, 2, Author              
Lampert, CH, Author              
Gretton, A2, 3, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2011-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.patrec.2011.02.011
BibTex Citekey: BlaschkoSBLG2011
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Pattern Recognition Letters
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Amsterdam : North-Holland
Pages: - Volume / Issue: 32 (11) Sequence Number: - Start / End Page: 1572 - 1583 Identifier: ISSN: 0167-8655
CoNE: /journals/resource/954925484685