Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

Nonnegative Matrix Factorization (NMF) has emerged as a powerful tool in data analysis, particularly noted for its ability to produce parts‐based, interpretable representations from high-dimensional ...

Abstract: Recommender systems (RSs) have gained significant attention for their ability to model user preferences and predict future trends. Collaborative filtering (CF), particularly through ...

Abstract: Recommender systems are essential in digital services for helping users find relevant items. One of the main challenges faced by these systems is the problem of sparsity, where limited ...

This package contains a data structure that wraps a matrix of matrices or factorizations and acts like the matrix resulting from concatenating the input matrices without allocating further memory.

I have been profiling FEA simulations that use HHT timestepper and PardisoMKL solver (see kcachegrind image below). I noticed the ChDirectSolverLS::Setup() function, in particular the factorization ...

Matrix decomposition is an area of linear algebra which is focused on expressing a matrix as a product of matrices with prescribed properties. (Photo credit: Merino et al., 2024) Imagine discovering ...

Computing the inverse of a matrix is one of the most important operations in machine learning. If some matrix A has shape n-by-n, then its inverse matrix Ai is n-by-n and the matrix product of Ai * A ...