IEEE

Distance-Based Logistic Matrix Factorization

Abstract: Matrix factorization is a central paradigm in matrix completion and collaborative filtering. Low-rank factorizations have been extremely successful in reconstructing and generalizing ...
GitHub

non-negative-matrix-factorization

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the ...
Frontiers

Collaborative filtering based on nonnegative/binary matrix factorization

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...
C&EN

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...
The New York Times

Here’s Why ‘The Matrix’ Is More Relevant Than Ever

One scene reflects the themes — A.I., fake news, transgender lives and Gen X — that make the film a classic. By Alissa Wilkinson Neo, the hero of “The Matrix,” is sure he lives in 1999. He has a green ...
IEEE

Quadratic Matrix Factorization With Applications to Manifold Learning

Abstract: Matrix factorization is a popular framework for modeling low-rank data matrices. Motivated by manifold learning problems, this paper proposes a quadratic matrix factorization (QMF) framework ...
GitHub

non-negative-matrix-factorization

Python PyTorch (GPU) and NumPy (CPU)-based port of Févotte and Dobigeon's robust-NMF algorithm appearing in "Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization." ...