Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization
SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020 We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction.
Srinivas Eswar +5 more
semanticscholar +3 more sources
Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding. [PDF]
PLoS ONEText embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose.
Mingming Li +3 more
doaj +2 more sources
Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering [PDF]
IEEE Access, 2019 As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity.
Wei Yan +3 more
doaj +2 more sources
Symmetric Nonnegative Matrix Factorization Based on Box-Constrained Half-Quadratic Optimization
IEEE Access, 2020 Nonnegative Matrix Factorization (NMF) based on half-quadratic (HQ) functions was proven effective and robust when dealing with data contaminated by continuous occlusion according to the half-quadratic optimization theory.
Bo-Wei Chen
doaj +2 more sources
An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation [PDF]
Symmetry, 2020 Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering.
Peitao Wang +7 more
semanticscholar +2 more sources
A Symmetric Rank-One Quasi-Newton Method for Nonnegative Matrix Factorization [PDF]
ISRN Applied Mathematics, 2014 As is well known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing, signal processing, and so forth. In this paper, an algorithm on nonnegative matrix approximation is proposed.
Lai, Shu-Zhen +2 more
openaire +5 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
semanticscholar +4 more sources
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality [PDF]
IEEE Transactions on Signal Processing, 2017 Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection, and image segmentation.
Songtao Lu, Mingyi Hong, Zhengdao Wang
semanticscholar +6 more sources
Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering
Pattern Recognition, 2022 Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the limited supervised information for the construction of ...
Jingxing Yin +4 more
semanticscholar +4 more sources
WSNMF: Weighted Symmetric Nonnegative Matrix Factorization for attributed graph clustering
NeurocomputingIn 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.
K. Berahmand +4 more
semanticscholar +3 more sources