Kernel Hyperalignment
author: Peter J. Ramadge,
Department of Electrical Engineering, Princeton University
published: Jan. 14, 2013, recorded: December 2012, views: 2707
published: Jan. 14, 2013, recorded: December 2012, views: 2707
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
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 conducted experiments using real-world, multi-subject fMRI data.
See Also:
Download slides: machine_ramadge_kernel_01.pdf (195.0 KB)
Download article: machine_ramadge_kernel_01.pdf (370.8 KB)
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: