DGCCA-pytorch:

A Pytorch Implementation of Deep Generalized Canonical Correlation Analysis as described in:

Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, and Raman Arora. Deep Generalized Canonical Correlation Analysis. The 4th Workshop on Representation Learning for NLP. 2019 (Paper-link)

Deep Generalized Canonical Correlation Analysis:

Generalized Canonical Correlation Analysis (GCCA) is a method which corresponds to solving an optimization problom objective to find the best linear shared space called G for the J view of a data

DeepGCCA is a non-linear version of GCCA which uses neural networks as the feature extractor functions instead of linear transformers. DGCCA is some how exention of DeepCCA for more than two views though it has a different objective function.

• figures source Deep Generalized Canonical Correlation Analysis - Arxiv 1702.02519

Pseudocode algorithm:

Pseudocode algorithm based on the paper,

Example:

Synthatic Data: (synth data generator)

DGCCA Latent space for views:

• figures source Deep Generalized Canonical Correlation Analysis - Arxiv 1702.02519

Prerequest:

• Python 3.6>=
• Pytorch 1.4 >= (should also work with >=1.0)
• Numpy
• Scipy
• Seanborn

Other Implementations:

• Theano Implementation By Adiran Benton.

Notes:

check list:

• cuda test
• Varient Batch sizes test

to do:

• Nan gradient/update rules (Famous issue of Deep CCA - based models, like DeepCCA Nan outputs)
• More numerical stabilization for varient Architectures

Warmest thanks to Mr. Adrian Benton for his kind helps.