Hi everyone, I am doing some standardization using standardScaler on data from VectorAssembler which is represented as sparse vectors. I plan to fit a regularized model. However, standardScaler does not allow the mean to be subtracted from sparse vectors. It will only divide by the standard deviation, which I understand is to keep the vector sparse. Thus I am trying to convert my sparse vectors into dense vectors, but this may not be worthwhile.
So my questions are: Is subtracting the mean during standardization only important when working with dense vectors? Does it not matter for sparse vectors? Is just dividing by the standard deviation with sparse vectors equivalent to also dividing by standard deviation w and subtracting mean with dense vectors? Thank you, Tobi
