DeepGCCA-pytorch

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DeepGCCA-pytorch

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An implementation of Deep Generalized Canonical Correlation Analysis (DGCCA or Deep GCCA) with pytorch.

DeepGCCA-pytorch 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# 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)](https://www.aclweb.org/anthology/W19-4301/)

# 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.

![](https://raw.githubusercontent.com/arminarj/DeepGCCA-pytorch/master/img/DGCCA.jpg)
![](https://raw.githubusercontent.com/arminarj/DeepGCCA-pytorch/master/img/GCCA-DGCCA-Benton.jpg)
 - figures source [Deep Generalized Canonical Correlation Analysis - Arxiv 1702.02519](https://arxiv.org/abs/1702.02519)

## Pseudocode algorithm:

Pseudocode algorithm based on the paper,
![](https://raw.githubusercontent.com/arminarj/DeepGCCA-pytorch/master/img/psuedocode.jpg)

# Example:

Synthatic Data: [(synth data generator)](/synth_data.py)
![](https://raw.githubusercontent.com/arminarj/DeepGCCA-pytorch/master/img/Synth-data.jpg)

DGCCA Latent space for views:
![](https://raw.githubusercontent.com/arminarj/DeepGCCA-pytorch/master/img/Lantent-space-views.jpg)

- figures source [Deep Generalized Canonical Correlation Analysis - Arxiv 1702.02519](https://arxiv.org/abs/1702.02519)

# Prerequest:

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

# Other Implementations:

- [Theano Implementation](https://bitbucket.org/adrianbenton/dgcca-py3/src/master/) 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](https://github.com/Michaelvll/DeepCCA))
 - More numerical stabilization for varient Architectures
 
 Warmest thanks to Mr. Adrian Benton for his kind helps.

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