Results 11 to 20 of about 2,052,954 (190)
Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix [PDF]
Algorithms, 2019 The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a ...
Paola Favati +3 more
doaj +8 more sources
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 +3 more
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A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality [PDF]
IEEE Transactions on Signal Processing, 2017 IEEE Transactions on Signal Processing (to appear)
Songtao Lu +2 more
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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 +2 more
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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 +4 more
openaire +3 more sources
Adaptive computation of the Symmetric Nonnegative Matrix Factorization (NMF)
, 2018 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 (1) +3 more
openaire +2 more sources
Symmetric Nonnegative Matrix Trifactorization [PDF]
, 2022 The Symmetric Nonnegative Matrix Trifactorization (SN-Trifactorization) is a factorization of an $n \times n$ nonnegative symmetric matrix $A$ of the form $BCB^T$, where $C$ is a $k \times k$ symmetric matrix, and both $B$ and $C$ are required to be ...
Bukovšek, Damjana Kokol +4 more
core +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 +3 more
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Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization. [PDF]
Comput Soc Netw, 2017 Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities.We propose a divide-and-conquer strategy to discover hierarchical community ...
Du R, Kuang D, Drake B, Park H.
europepmc +4 more sources
CASNMF: A Converged Algorithm for symmetrical nonnegative matrix factorization [PDF]
Neurocomputing, 2018 Abstract Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis.
Li-Ping Tian 0001 +4 more
openaire +2 more sources