2012
NIPS
NeurIPS 2012
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.
🌉
Interdisciplinary Bridge
— Healthcare & Medicine and Machine Learning
🧭
Keyword Pioneer
— hyperalignment
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
📈
Trend Setter
— Kernel Methods
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Application Areas > Domain Adaptation
Healthcare & Medicine > Research > Bioinformatics
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Bayesian & Probabilistic > Kernel Methods
Mathematics & Optimization > Optimization > Kernel Methods
Interdisciplinary > Science > Neuroscience