Download MNIST dataset to the same folder that the MATLAB code are as follows.
Step 1: Download from http://yann.lecun.com/exdb/mnist the following 4 files:
o train-images-idx3-ubyte.gz o train-labels-idx1-ubyte.gz o t10k-images-idx3-ubyte.gz o t10k-labels-idx1-ubyte.gz Step 2: Unzip these 4 files by executing: o gunzip train-images-idx3-ubyte.gz o gunzip train-labels-idx1-ubyte.gz o gunzip t10k-images-idx3-ubyte.gz o gunzip t10k-labels-idx1-ubyte.gz
If unzipping with WinZip, make sure the file names have not been changed by WinZip.
Step 3: Run DeepFS2.m
Step 4: The program will return two options.
Enter 1 to run DeepFS to select features, or Enter 2 to first select features using DeepFS and then train a Deep Boltzmann machine (DBM) on the selected data.
The principle of DeepFS is described in [1]. The DBM in [2] is used in this code.
[1] Aboozar Taherkhani, Georgina Cosma, T. M McGinnity, Deep-FS: A feature selection algorithm for Deep Boltzmann Machines, Neurocomputing, Volume 322, 2018, Pages 22-37, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2018.09.040. (http://www.sciencedirect.com/science/article/pii/S0925231218311020) Keywords: Deep Boltzmann Machine; Deep learning; Deep Neural Networks; Feature selection; Restricted Boltzmann Machine; Generative models; Missing features
[2] Learning Deep Boltzmann Machines, http://www.cs.toronto.edu/~rsalakhu/DBM.html
Please e-mail us if you find bugs.
Dr Aboozar Taherkhani:
aboozar.taherkhani@ntu.ac.uk
Dr Georgina Cosma georgina.cosma@ntu.ac.uk