Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
be a way to remove all kinds of relationships from the data so that we could have independent features for modeling. This is not something that SVD can do — SVD finds features that capture the most variance in the data only by taking linear combinations of existing features. Non-linear