Results 291 to 300 of about 142,276 (359)

Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

IEEE Transactions on Computational Social Systems, 2023
The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data ...
Ping Deng, Hongjun Wang, Tianrui Li, H Zhao, Yanping Wu   +4 more
semanticscholar   +1 more source

Non-negative wavelet matrix factorization-based bearing fault intelligent classification method

Measurement science and technology, 2023
There are more and more bearing fault types under considering the fault location and degree, and the corresponding fault classification task is becoming increasingly heavy.
Zhilin Dong, Dezun Zhao, Lingli Cui
semanticscholar   +1 more source

Urban Traffic Pattern Analysis and Applications Based on Spatio-Temporal Non-Negative Matrix Factorization

IEEE transactions on intelligent transportation systems (Print), 2022
Analyzing the traffic state of large citywide networks is an inherently difficult task. Various data issues, traffic signals, stops signs and other flow inhibitors of the network-level traffic state make the analysis more difficult than that under the ...
Yang Wang, Yong Zhang, Lixun Wang, Yongli Hu, Baocai Yin   +4 more
semanticscholar   +1 more source

Multiobjective Sparse Non-Negative Matrix Factorization

IEEE Transactions on Cybernetics, 2019
Non-negative matrix factorization (NMF) is becoming increasingly popular in many research fields due to its particular properties of semantic interpretability and part-based representation. Sparseness constraints are usually imposed on the NMF problems in order to achieve potential features and sparse representation.
Maoguo Gong, Xiangming Jiang, Hao Li, Kay Chen Tan   +3 more
openaire   +2 more sources

Farness preserving Non-negative matrix factorization

2014 IEEE International Conference on Image Processing (ICIP), 2014
Dramatic growth in the volume of data made a compact and informative representation of the data highly demanded in computer vision, information retrieval, and pattern recognition. Non-negative Matrix Factorization (NMF) is used widely to provide parts-based representations by factorizing the data matrix into non-negative matrix factors.
Babaee, Mohammadreza, Bahmanyar, Reza, Rigoll, Gerhard, Datcu, Mihai   +3 more
openaire   +1 more source

Robust non-negative matrix factorization

Frontiers of Electrical and Electronic Engineering in China, 2011
Non-negative matrix factorization (NMF) is a recently popularized technique for learning parts-based, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted.
Lijun Zhang, Zhengguang Chen, Miao Zheng, Xiaofei He   +3 more
openaire   +1 more source

Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering

IEEE Transactions on Cybernetics, 2020
Multiview data processing has attracted sustained attention as it can provide more information for clustering. To integrate this information, one often utilizes the non-negative matrix factorization (NMF) scheme which can reduce the data from different ...
Zuyuan Yang, Naiyao Liang, Wei Yan, Zhenni Li, Shengli Xie   +4 more
semanticscholar   +1 more source

Meta Path-Aware Recommendation Method Based on Non-Negative Matrix Factorization in LBSN

IEEE Transactions on Network and Service Management, 2022
Location-based social networks (LBSN) is a new type of heterogeneous information network (HIN). The check-in data usually has the characteristics of a large amount of data and high sparsity.
Zheng Yang, Zeyu Wang, Lei Guo, Wenju Liu, Shimin Sun   +4 more
semanticscholar   +1 more source

Dropout non-negative matrix factorization

Knowledge and Information Systems, 2018
Non-negative matrix factorization (NMF) has received lots of attention in research communities like document clustering, image analysis, and collaborative filtering. However, NMF-based approaches often suffer from overfitting and interdependent features which are caused by latent feature co-adaptation during the learning process.
Zhicheng He, Jie Liu, Caihua Liu, Yuan Wang, Airu Yin, Yalou Huang   +5 more
openaire   +1 more source

Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection

IEEE Transactions on Automation Science and Engineering
Community is a fundamental and highly desired pattern in a Large-scale Undirected Network (LUN). Community detection is a vital issue when LUN representation learning is performed.
Zhigang Liu, Xin Luo, Mengchu Zhou
semanticscholar   +1 more source