@inproceedings{benton-etal-2019-deep,
title = "Deep Generalized Canonical Correlation Analysis",
author = "Benton, Adrian and
Khayrallah, Huda and
Gujral, Biman and
Reisinger, Dee Ann and
Zhang, Sheng and
Arora, Raman",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4301",
doi = "10.18653/v1/W19-4301",
pages = "1--6",
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.",
}

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%0 Conference Proceedings
%T Deep Generalized Canonical Correlation Analysis
%A Benton, Adrian
%A Khayrallah, Huda
%A Gujral, Biman
%A Reisinger, Dee Ann
%A Zhang, Sheng
%A Arora, Raman
%S Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F benton-etal-2019-deep
%X 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.
%R 10.18653/v1/W19-4301
%U https://aclanthology.org/W19-4301
%U https://doi.org/10.18653/v1/W19-4301
%P 1-6

##### Markdown (Informal)

[Deep Generalized Canonical Correlation Analysis](https://aclanthology.org/W19-4301) (Benton et al., RepL4NLP 2019)

##### ACL

- Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, and Raman Arora. 2019. Deep Generalized Canonical Correlation Analysis. In
*Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)*, pages 1–6, Florence, Italy. Association for Computational Linguistics.