nips nips2012 nips2012-167 nips2012-167-reference knowledge-graph by maker-knowledge-mining
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Author: Alexander Lorbert, Peter J. Ramadge
Abstract: We offer a regularized, kernel extension of the multi-set, orthogonal Procrustes problem, or hyperalignment. Our new method, called Kernel Hyperalignment, expands the scope of hyperalignment to include nonlinear measures of similarity and enables the alignment of multiple datasets with a large number of base features. With direct application to fMRI data analysis, kernel hyperalignment is well-suited for multi-subject alignment of large ROIs, including the entire cortex. We report experiments using real-world, multi-subject fMRI data. 1
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