I applied svd method to my matrix as a dimensionality reduction method.
My main aim is to get higher classification rate.
[U, Sig, V]=svd(X);
So my question here is that how can i determine the number of singular values that i should take for creating a new data for classification?
As i understand from the above figure i have to take approximately 250 singular values that it counts for 95% of my data.
So should I take first 250 singular values for creating a new data for classification? How can i interpret the figure above?
Thanks in advance.