Does anyone know how to perform svd operation on a sparse matrix in python? It seems that there is no such functionality provided in scipy.sparse.linalg.
I am using truncated SVD from scikit-learn package.
In the definition of SVD, an original matrix A is approxmated as a product A ≈ UΣV* where U and V have orthonormal columns, and Σ is non-negative diagonal.
I need to get …
I am trying to apply PCA on huge sparse matrix, in the following link it says that randomizedPCA of sklearn can handle sparse matrix of scipy sparse format.
Apply PCA on very large sparse matrix
However, I always get error. …
For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are the functions to do the inverse?
I searched, but got no idea what keywords should be the right hit.