This code implement the Bayesian Posterior Inference for the Convolutional Factor Analysis via Gibbs sampling

@author: Zhe Gan (zhe.gan@duke.edu), Duke ECE, 4.30.2015

1. B. Chen, G. Polatkan, G. Sapiro, D. Dunson and L. Carin, "
The Hierarchical Beta Process for Convolutional Factor Analysis and 
Deep Learning", Proc. Int. Conf. Machine Learning (ICML), 2011

2. B. Chen, G. Polatkan, G. Sapiro, D. Blei, D. Dunson and L. Carin, 
"Deep Learning with Hierarchical Convolutional Factor Analysis", IEEE 
Trans. Pattern Analysis & Machine Intelligence, 2013.

Please note that this code should be used at your own risk. There is no implied guarantee that it will not do anything stupid. Permission is granted to use and modify the code.

The project.pdf file includes all the details needed to understand the code. The corresponding .tex file can be found in the directory "latex", including experimental results.

Go to the directory "code", Run "demo_mnist.py" & "demo_face.py" directly to reproduce the results. For comparison with PCA, Run "pca.py".