Deep-FS/ReadME.txt at master · gcosma/Deep-FS · GitHub

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READ ME - Instructions on how to run the code.
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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] [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)


[2] Learning Deep Boltzmann Machines, http://www.cs.toronto.edu/~rsalakhu/DBM.html


Please e-mail me if us find bugs.
Dr Aboozar Taherkhani:
[email protected]

Dr Georgina Cosma
[email protected]