Results 31 to 40 of about 23,872 (265)

Negative Binomial Matrix Factorization [PDF]

IEEE Signal Processing Letters, 2020
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF
Olivier Gouvert, Thomas Oberlin, Cédric Févotte   +2 more
openaire   +3 more sources

On Rationality of Nonnegative Matrix Factorization [PDF]

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. NMF has a wide variety of applications, including bioinformatics, chemometrics, communication complexity, machine learning, polyhedral combinatorics, among many others ...
Dmitry Chistikov 0001, Stefan Kiefer, Ines Marusic, Mahsa Shirmohammadi, James Worrell 0001   +4 more
openaire   +2 more sources

A Novel Matrix Completion Model Based on the Multi-Layer Perceptron Integrating Kernel Regularization

IEEE Access, 2021
Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of
Xuan Hu, Yongming Han, Zhiqiang Geng
doaj   +1 more source

Sparse Matrix Factorization

CoRR, 2013
We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions. This problem can be viewed as a simplification of the deep learning problem where finding a factorization corresponds to finding edges in different layers and values of hidden units.
Behnam Neyshabur, Rina Panigrahy
openaire   +2 more sources

Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug–target interactions prediction

BMC Bioinformatics, 2022
Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching
Junjun Zhang, Minzhu Xie
doaj   +1 more source

Cauchy nonnegative matrix factorization [PDF]

2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015
Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for nonnegative data. It enjoys a rich background, both from an optimization and probabilistic signal processing viewpoint. In this study, we propose a new cost-function for NMF fitting, which is introduced as arising naturally when adopting a Cauchy process model for ...
Liutkus, Antoine, Fitzgerald, Derry, Badeau, Roland   +2 more
openaire   +2 more sources

Continuous Semi-Supervised Nonnegative Matrix Factorization

Algorithms, 2023
Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom, Xiaofu Ding, Feng Liu, Anand Somayajula, Deanna Needell   +4 more
doaj   +1 more source

Uncovering community structures with initialized Bayesian nonnegative matrix factorization. [PDF]

PLoS ONE, 2014
Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks.
Xianchao Tang, Tao Xu, Xia Feng, Guoqing Yang   +3 more
doaj   +1 more source

Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

Cybernetics and Information Technologies, 2014
In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
doaj   +1 more source

Optimizing and Factorizing the Wilson Matrix

The American Mathematical Monthly, 2022
The Wilson matrix, W, is a 4 × 4 unimodular symmetric positive definite matrix of integers that has been used as a test matrix since the 1940’s, owing to its mild ill-conditioning. We ask how close W is to being the most ill-conditioned matrix in its class, with or without the requirement of positive definiteness.
Nicholas J. Higham, Matthew C. Lettington   +1 more
openaire   +2 more sources