Nature

Nonnegative Matrix Factorization and Its Applications

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 ...
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 ...
IEEE

Sparse Matrix Factorization Using Diagonal Pivoting for Power Distribution Network Applications

Abstract: Power distribution network (PDN) applications are real-time applications with complex data represented by sparse matrices that, depending on the application, may be positive definite or ...
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 ...
Frontiers

Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks

1 Institute for Theoretical Physics, University of Bremen, Bremen, Germany 2 Institute of Electrodynamics and Microelectronics (ITEM.ids), University of Bremen, Bremen, Germany Considering biological ...
Scientific Research Publishing

Pauca, V.P., Piper, J. and Plemmons, R.J. (2006) Nonnegative Matrix Factorization for Spectral Data Analysis. Linear Algebra and Its Applications, 416, 29-47.

ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...