Matrix factorization methods compress the original data matrix A in
R^{f,N} with f features and N samples into two parts,
namely A = B C with B in R^{f,k}, Cin R^{k,
N}. The function estimateDimension estimates k based on a noise
model estimated from a scrambled version of the original data matrix.