TY - GEN
T1 - Kernel hyperalignment
AU - Lorbert, Alexander
AU - Ramadge, Peter J.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84877735093&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877735093&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84877735093
SN - 9781627480031
T3 - Advances in Neural Information Processing Systems
SP - 1790
EP - 1798
BT - Advances in Neural Information Processing Systems 25
T2 - 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Y2 - 3 December 2012 through 6 December 2012
ER -