Zobrazeno 1 - 10
of 32 579
pro vyhledávání: '"Nonnegative Matrix Factorization"'
Non-negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi-Nonnegative Matrix Factorization (SNF), extends
Externí odkaz:
http://arxiv.org/abs/2410.16453
Publikováno v:
Journal of Zhengzhou University(Natural Science Edition),2022,54 (05), 43-48
A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better distinguish normal samples from abnormal samples in an error-driven wa
Externí odkaz:
http://arxiv.org/abs/2410.15306
Orthogonal nonnegative matrix factorization (ONMF) has become a standard approach for clustering. As far as we know, most works on ONMF rely on the Frobenius norm to assess the quality of the approximation. This paper presents a new model and algorit
Externí odkaz:
http://arxiv.org/abs/2410.07786
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusio
Externí odkaz:
https://doaj.org/article/7e48120f9ab8476ba2f4fb8abc17ccaa
Autor:
Guo, Youdong, Holy, Timothy E.
Non-negative matrix factorization (NMF) is a key technique for feature extraction and widely used in source separation. However, existing algorithms may converge to poor local minima, or to one of several minima with similar objective value but diffe
Externí odkaz:
http://arxiv.org/abs/2408.09013
Autor:
Carbonero, Daniel1,2,3 (AUTHOR), Noueihed, Jad1,2,3 (AUTHOR), Kramer, Mark A.2,4 (AUTHOR), White, John A.1,2,3 (AUTHOR) jwhite@bu.edu
Publikováno v:
Scientific Reports. 11/14/2024, Vol. 12 Issue 1, p1-17. 17p.
Autor:
Dehghanpour, Ja'far1 (AUTHOR), Mahdavi-Amiri, Nezam1 (AUTHOR) nezamm@sharif.edu
Publikováno v:
Annals of Operations Research. Aug2024, Vol. 339 Issue 3, p1481-1497. 17p.
When applying nonnegative matrix factorization (NMF), generally the rank parameter is unknown. Such rank in NMF, called the nonnegative rank, is usually estimated heuristically since computing the exact value of it is NP-hard. In this work, we propos
Externí odkaz:
http://arxiv.org/abs/2407.00706
Publikováno v:
Lecture Notes in Networks and Systems, Volume 635 LNNS, Pages 313 - 318, 2023
Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloadi
Externí odkaz:
http://arxiv.org/abs/2406.10235
Nonnegative matrix factorization (NMF) is a popular method in machine learning and signal processing to decompose a given nonnegative matrix into two nonnegative matrices. In this paper, to solve NMF, we propose new algorithms, called majorization-mi
Externí odkaz:
http://arxiv.org/abs/2405.11185