Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
data to extract high variance and linearly independent features.  SVD can decompose any nxm matrix A of rank r into a product of 3 matrices: where U is nxr, 𝝨 is an rxr diagonal matrix, and V is mxr. The columns of U are referred to as singular vectors and are the high variance, linearly