Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning

arXiv:2310.01012v3 Announce Type: replace
Abstract: The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning. Regularised linear CCA methods can be seen to generalise Partial Least Squares (PLS) and be unified with a Generalized Eigenvalue Problem (GEP) framework. However, classical algorithms for these linear methods are computationally infeasible for large-scale data. Extensions to Deep CCA show great promise, but current training procedures are slow and complicated. First we propose a novel unconstrained objective that characterizes the top …

abstract algorithms analysis arxiv canonical correlation cs.ai cs.lg eigenvalue family framework generalized however least linear self-supervised learning squares stat.ml stochastic supervised learning type