Welcome to the nlp-2.1-matrix-decomposition repository! This project provides a collection of algorithms for matrix decomposition, a fundamental concept in linear algebra. Whether you're working on ...
Abstract: Non-negative Matrix Factorization (NMF) has been an ideal tool for machine learning. Non-negative Matrix Tri-Factorization (NMTF) is a generalization of NMF that incorporates a third ...
Abstract: The low-rank property of seismic data has been successfully used for attenuating seismic random noise using a rank-reduction processing; however, traditional rank-reduction methods based on ...
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...
Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...
Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...
Dr. James McCaffrey of Microsoft Research guides you through a full-code, step-by-step tutorial on "one of the most important operations in machine learning." Computing the inverse of a matrix is one ...
Following that, we'll look at Probabilistic Matrix Factorization (PMF), which is a more sophisticated Bayesian method for predicting preferences. Having detailed the PMF model, we'll use PyMC for MAP ...
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