Learn some tips and tricks for improving the speed and accuracy of matrix decompositions in R or Python, two popular languages for statistical programming.

Learn how to implement SVD and NMF methods to decompose user-item rating matrices and improve recommendations using Python or R. Skip to main content LinkedIn Articles ...

This repository provides Python implementations for Non-negative Matrix Factorization (NMF) using the Multiplicative Update (MU) algorithm. Two initialization methods are supported: random ...

It takes a condition as input and returns a boolean array indicating which elements satisfy the condition. By using this boolean array as an index, you can extract the filtered elements from the ...

Check data input in data_matrix.txt. Note: The original input data is huge and could not be uploadded to GitHub. The code is also good for n rows data input when n is huge. The code in matrix_multiply ...

If the input dimension is high Principal Component Algorithm can be used to speed up our machines. It is a projection method while retaining the features of the original data. In this article, we will ...