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

Pattern Recognition Letters

Volumn 32, Issue 11, 2011, Pages 1572-1583

  Blaschko, Matthew B ^a   Shelton, Jacquelyn A ^b   Bartels, Andreas ^c,d   Lampert, Christoph H ^e   Gretton, Arthur ^d,f  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b FRANKFURT INSTITUTE FOR ADVANCED STUDIES   (Germany)

^c UNIVERSITY OF TÜBINGEN   (Germany)

^d MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^e INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA   (Austria)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Canonical correlation analysis; fMRI; Semi supervised learning

Indexed keywords

BRAIN REGIONS; CANONICAL CORRELATION ANALYSIS; DATA MANIFOLDS; FISHER LINEAR DISCRIMINANT ANALYSIS; FMRI; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HIGHLY-CORRELATED; HUMAN BRAIN; KERNEL CANONICAL CORRELATION ANALYSIS; LAPLACIANS; PRINCIPAL COMPONENTS ANALYSIS; ROBUST ESTIMATE; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SMALL SAMPLE EFFECTS; SUBSPACE LEARNING; SUBSPACE TECHNIQUES; VIDEO CONTENTS; VISUAL-PROCESSING; WELL-ALIGNED;

BRAIN; DISCRIMINANT ANALYSIS; FACE RECOGNITION; LAPLACE TRANSFORMS; MAGNETIC RESONANCE IMAGING; RESONANCE; SUPERVISED LEARNING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79957514113     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.02.011     Document Type: Article

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