MATLAB code for the paper "On the Importance of Hidden Bias and Hidden Entropy in Representational Efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines" by A. Isabekov and E. Erzin, published in Neural Networks, Vol. 105, September 2018, Pages 405-418.
Training GBPRBM model using Contrastive Divergence alogrithm for three-dimensional data (number of visible units is equal to 3).
Passing the value of 1 to the function enables loading pretrained weights from LBG-like clustering alogirthm. Pretrained weights are stored in "GeometryLBG.mat" file.
>> Synthetic_Data_3V_Train_GBPRBM(1)
Invoking the function without any arguments enables random initialization of the weights:
>> Synthetic_Data_3V_Train_GBPRBM
To obtain "GeometryLBG.mat" file, run
>> GBPRBM_LBG_Pretraining
Executing PaperFig_VPDF_1V_2V_3V.m will create
Executing GBPRBM_Plot_HEntropy_vs_HBias_1H_Analysis.m will create
Executing GBPRBM_Plot_HEntropy_vs_HBias_2H_Analysis.m will create
Executing GBPRBM_Plot_HEntropy_vs_HBias_3H_Analysis.m will create
