# Deep Generalized Canonical Correlation Analysis

## TL;DR

Deep Generalized Canonical Correlation Analysis (DGCCA) is a novel method that learns nonlinear, multiview data representations, outperforming existing techniques in phonetic transcription and social media tasks by combining deep learning with multiple data sources.

## Contribution

DGCCA introduces the first nonlinear multiview CCA method that effectively integrates many data views using deep learning and an efficient stochastic optimization algorithm.

## Key findings

- DGCCA outperforms existing methods in phonetic transcription.
- DGCCA improves hashtag and friend recommendation accuracy.
- DGCCA performs comparably to linear multiview techniques on other tasks.

## 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.