I have a set of feature values in a matrix where a single column represents feature values for each 3D point (i.e. each row). I need to use that data for training a SVM model. For doing that, I first need to normalize the feature values. But the issue is, applying any normalisation technique gives same values for the particular column of the feature value matrix. For example, if I use min-max normalisation, all the values in a particular column for the feature matrix, will be same maybe because of the high variance in the data. For instance, the maximum and minimum value for particular column is:
Max value for 1st column of features: 7.7409e+11
Min value for 1st column of features: -9.3142e+11
A sample of values after scaling:
---------------
Col1 Col2
---------------
0.5461 | 0.0293
0.5461 | 0.0293
0.5461 | 0.0293
0.5461 | 0.0293
0.5461 | 0.0293
Due to such a high range, I am unable to obtain any results. I have also tried a different normalisation technique called as decimal-scaling normalisation. The formula for the method is:
MATLAB CODE:
j = round(log10(max(data(:,1))))+1;
normalisation(:,i-3) = data(:, i)/(10.^j);
But i'm not sure if this is the right approach. Please help me with the normalisation technique which best suits such data.