Results 61 to 70 of about 4,116 (162)

Multi-constraint non-negative matrix factorization for community detection: orthogonal regular sparse constraint non-negative matrix factorization

Complex & Intelligent Systems
Community detection is an important method to analyze the characteristics and structure of community networks, which can excavate the potential links between nodes and further discover subgroups from complex networks.
Zigang Chen, Qi Xiao, Tao Leng, Zhenjiang Zhang, Ding Pan, Yuhong Liu, Xiaoyong Li   +6 more
doaj   +1 more source

Discriminative Multiview Nonnegative Matrix Factorization for Classification

IEEE Access, 2019
Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination.
Weihua Ou, Jianping Gou, Quan Zhou, Shiming Ge, Fei Long   +4 more
doaj   +1 more source

Graph Regularized Constrained Non-Negative Matrix Factorization With Lₚ Smoothness for Image Representation

IEEE Access, 2020
Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years.
Zhenqiu Shu, Zonghui Weng, Yunmeng Zhang, Cong-Zhe You, Zhen Liu   +4 more
doaj   +1 more source

Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

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, Bob Zhang, Zuyuan Yang, Shengli Xie   +3 more
doaj   +1 more source

A Constrained Sparse Algorithm for Nonnegative Matrix Factorization

工程科学与技术, 2015
:Aiming at the lack of sparseness of factorization matrix in the nonnegative matrix factorization (NMF) algorithm,a new constrained NMF algorithm was proposed.A sparseness constraint was added to the original nonnegative matrix factorization (NMF ...
李臣明, 张师明, 李昌利
doaj  

Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization

Tongxin xuebao, 2020
To address problems that the effectiveness of feature learned from real noisy data by classical nonnegative matrix factorization method,a novel sparsity induced manifold regularized convex nonnegative matrix factorization algorithm (SGCNMF) was proposed ...
Feiyue QIU, Bowen CHEN, Tieming CHEN, Guodao ZHANG   +3 more
doaj   +2 more sources

Parallel Nonnegative Matrix Factorization with Manifold Regularization

Journal of Electrical and Computer Engineering, 2018
Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices.
Fudong Liu, Zheng Shan, Yihang Chen
doaj   +1 more source

Improved Graph-Regularized Discriminative Nonnegative Matrix Factorization for Semi-Supervised Clustering

IEEE Access
Nonnegative matrix factorization (NMF) is an effective dimensionality reduction and representation learning technique that captures the intrinsic structure of nonnegative data by learning low-dimensional, parts-based representations.
Xuzhu Shen, Jie Li
doaj   +1 more source

A Label-Embedding Online Nonnegative Matrix Factorization Algorithm

IEEE Access, 2019
Nonnegative matrix factorization is a widely used data processing method, which has been applied in many fields, such as data dimension reduction and feature extraction.
Zhibo Guo, Ying Zhang
doaj   +1 more source

Nonnegative Matrix Factorization [PDF]

, 2013
Matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data. SVD is a classical method for matrix factorization, which gives the optimal low-rank approximation to a real-valued matrix in terms of the squared error.
Ke-Lin Du, M. N. S. Swamy
openaire   +1 more source