Multiview Representation Learning for a Union of Subspaces
Machine Learning
2020-01-01 v1 Machine Learning
Abstract
Canonical correlation analysis (CCA) is a popular technique for learning representations that are maximally correlated across multiple views in data. In this paper, we extend the CCA based framework for learning a multiview mixture model. We show that the proposed model and a set of simple heuristics yield improvements over standard CCA, as measured in terms of performance on downstream tasks. Our experimental results show that our correlation-based objective meaningfully generalizes the CCA objective to a mixture of CCA models.
Cite
@article{arxiv.1912.12766,
title = {Multiview Representation Learning for a Union of Subspaces},
author = {Nils Holzenberger and Raman Arora},
journal= {arXiv preprint arXiv:1912.12766},
year = {2020}
}