Multiview Representation Learning for a Union of Subspaces

• URL: http://arxiv.org/abs/1912.12766v1
• Date: Mon, 30 Dec 2019 00:44:13 GMT
• Title: Multiview Representation Learning for a Union of Subspaces
• Authors: Nils Holzenberger and Raman Arora
• Abstract summary: We show that a proposed model and a set of simple mixtures yield improvements over standard CCA. Our correlation-based objective meaningfully generalizes the CCA objective to a mixture of CCA models.
• Score: 38.68763142172997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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