Kernel Hyperalignment
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 conducted experiments using real-world, multi-subject fMRI data.
Cite
Text
Lorbert and Ramadge. "Kernel Hyperalignment." Neural Information Processing Systems, 2012.Markdown
[Lorbert and Ramadge. "Kernel Hyperalignment." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/lorbert2012neurips-kernel/)BibTeX
@inproceedings{lorbert2012neurips-kernel,
title = {{Kernel Hyperalignment}},
author = {Lorbert, Alexander and Ramadge, Peter J.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {1790-1798},
url = {https://mlanthology.org/neurips/2012/lorbert2012neurips-kernel/}
}