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.
| Original language | English (US) |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 25 |
| Subtitle of host publication | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
| Pages | 1790-1798 |
| Number of pages | 9 |
| Volume | 3 |
| State | Published - Dec 1 2012 |
| Event | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: Dec 3 2012 → Dec 6 2012 |
Other
| Other | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
|---|---|
| Country/Territory | United States |
| City | Lake Tahoe, NV |
| Period | 12/3/12 → 12/6/12 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Signal Processing