Deep Generalized Canonical Correlation Analysis

Adrian Benton, Huda Khayrallah, Biman Gujral, Drew Reisinger, Sheng Zhang, Raman Arora

Abstract: We present Deep Generalized Canonical Correlation Analysis (DGCCA) – a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.

TL;DR: A multiview representation learning technique that can learn nonlinear mappings from arbitrarily many views to a shared semantic space -- Deep Generalized Canonical Correlation Analysis.

Conflicts: cs.jhu.edu, berkeley.edu

Keywords: Unsupervised Learning, Deep learning, Multi-modal learning

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1702.02519/code)