Hi Jay,
I understand that you want to remove the unit variance normalization from the “W"and "H” parameters that you obtained after performing the NNMF(Non-Negative Matrix Factorization) analysis on the normalized data, "EMG_unit_var".
The result of NNMF is two matrices: “W”, a feature matrix, and “H”, the coefficient matrix.
To denormalize the resulting matrices “W” and “H”,the denormalization formula is applied. For matrix “W”, the denormalization involves multiplying it by the row-wise standard deviation vector obtained from the “EMGo" matrix. Matrix “H” remains unchanged as it represents the coefficients.
In this case, I noticed that you have normalized the data and then performed NNMF on the transpose of the so obtained data, that is, the transpose of “EMG_unit_var”.This could cause an issue while denormalizing as the resulting “W” matrix cannot be multiplied with the standard deviation vector.
I recommend you take the transpose of the data and then normalize it before performing the NNMF analysis. Refer to the following modified code that incorporates this change:
load('EMGo.mat');
std_EMG = std(EMGo',0,2); %find the standard deviation of EMGo
EMG_unit_var = EMGo' ./ std_EMG; %divide by standard deviation for unit variance
k= 2; %assuming the rank is equal to 2
opt = statset('MaxIter',500,'Display','final'); %opions for nnmf
[W0,H0] = nnmf(EMG_unit_var ,k,'replicates',10,'options',opt,'algorithm','mult'); %set the initials of nnmf
opt = statset('Maxiter',1000,'Display','final'); %set options for the nnmf
[W,H] = nnmf(EMG_unit_var ,k,'w0',W0,'h0',H0,'options',opt, 'algorithm','als'); % find the W and H nnmf factors
W_denormalized = W .* std_EMG;
H_denormalized = H;
Hope this helps.
