Results 11 to 20 of about 1,917,955 (191)

Adaptive computation of the Symmetric Nonnegative Matrix Factorization (SymNMF)

SeMA Journal, 2020
Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is ...
P. Favati, G. Lotti, O. Menchi, F. Romani   +3 more
openaire   +4 more sources

On reduced rank nonnegative matrix factorization for symmetric nonnegative matrices [PDF]

Linear Algebra and its Applications, 2004
Let \(V\) be a nonnegative matrix. The nonnegative matrix factorization problem consists of finding nonnegative matrix factors \(W \in \mathbb{R}^{m,r}\) and \(H \in \mathbb{R}^{r,n}\) such that \(V \approx WH\). \textit{D. D. Lee} and \textit{H. S. Seung} [Unsupervised learning by convex and conic coding, Adv. Neural Inf. Process. Syst.
Catral, M., Han, Lixing, Neumann, Michael, Plemmons, R.J.   +3 more
openaire   +2 more sources

Efficient and Non-Convex Coordinate Descent for Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Signal Processing, 2016
Given a symmetric nonnegative matrix $A$ , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$ , usually with much fewer columns than $A$ , such that $A \approx HH^T$ . SymNMF can be used for data analysis and in particular for various clustering tasks.
Nicolas Gillis, Arnaud Vandaele, Qi Lei   +2 more
exaly   +3 more sources

WSNMF: Weighted Symmetric Nonnegative Matrix Factorization for attributed graph clustering

Neurocomputing
In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph clustering. Nevertheless, when applied to attributed graph clustering, it confronts notable challenges.
Razieh Sheikhpour, Yuefeng Li, Kamal Berahmand   +2 more
exaly   +3 more sources

Block Sparse Symmetric Nonnegative Matrix Factorization Based on Constrained Graph Regularization [PDF]

Jisuanji kexue, 2023
The existing algorithms based on symmetric nonnegative matrix factorization(SymNMF) are mostly rely on initial data to construct affinity matrices,and neglect the limited pairwise constraints,so these methods are unable to effectively distinguish similar
LIU Wei, DENG Xiuqin, LIU Dongdong, LIU Yulan
doaj   +1 more source

Off-diagonal symmetric nonnegative matrix factorization [PDF]

Numerical Algorithms, 2021
Symmetric nonnegative matrix factorization (symNMF) is a variant of nonnegative matrix factorization (NMF) that allows to handle symmetric input matrices and has been shown to be particularly well suited for clustering tasks. In this paper, we present a new model, dubbed off-diagonal symNMF (ODsymNMF), that does not take into account the diagonal ...
Moutier, François, Vandaele, Arnaud, Gillis, Nicolas   +2 more
openaire   +5 more sources

Self-Supervised Symmetric Nonnegative Matrix Factorization [PDF]

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering ...
Yuheng Jia, Hui Liu 0032, Junhui Hou, Sam Kwong, Qingfu Zhang 0001   +4 more
openaire   +3 more sources

Microbiome Data Analysis by Symmetric Non-negative Matrix Factorization With Local and Global Regularization

Frontiers in Molecular Biosciences, 2021
A network is an efficient tool to organize complicated data. The Laplacian graph has attracted more and more attention for its good properties and has been applied to many tasks including clustering, feature selection, and so on.
Junmin Zhao, Yuanyuan Ma, Lifang Liu
doaj   +1 more source

A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

IEEE Transactions on Knowledge and Data Engineering, 2021
The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that ...
Xiao Li 0009, Zhihui Zhu, Qiuwei Li, Kai Liu 0018   +3 more
openaire   +3 more sources

Orthogonal Symmetric Nonnegative Matrix Tri-Factorization

2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)
Nicolas Gillis
exaly   +2 more sources