Deep Generalized Canonical Correlation Analysis

Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann 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 combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic & articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.
Anthology ID:
W19-4301
Volume:
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
URL:
https://www.aclweb.org/anthology/W19-4301
DOI:
10.18653/v1/W19-4301
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PDF:
https://www.aclweb.org/anthology/W19-4301.pdf