truncated SVD decomposition problem
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Hi everyone,
I am coding an algorithm that has the truncated SVD inside and I am wondering if there is any way to fast perform the truncated SVD.
For example: I have a matrix and I need to perform the SVD decomposition that will result in three matrices U, S, V. In the matrix S, I only want to keep k eigenvalues (k columns) with the condition that these eigenvalues > THRESHOLD*Largest_eigenvalue.
Currently, I perform the truncated SVD by first computing the SVD 'econ' and then keeping the k columns. I wonder if there is any other faster way to do this.
P/S: I do not know the k number before having the S matrix. What I have before doing the decomposition is the THRESHOLD value.
Thank you very much in advance.
Vinh
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Answers (1)
Abhijeet Go-kar
on 31 Mar 2018
The faster way to do truncated SVD is to find its analogue with PCA and perform the matrix operations accordingly. Rather than worry about the threshold of eigenvalues, just give a comparison statement of the dimensions of the matrix in the code, and perform the SVD on the matrices.
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