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Kernel Hyperalignment
This video was recorded at Video Journal of Machine Learning Abstracts - Volume 3. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
