Results 31 to 40 of about 2,137 (155)

Robust Graph Regularized Nonnegative Matrix Factorization

IEEE Access, 2022
Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on
Qi Huang, Guodao Zhang, Xuesong Yin, Yigang Wang   +3 more
doaj   +1 more source

Community Detection Algorithm Based on Nonnegative Matrix Factorization and Improved Density Peak Clustering

IEEE Access, 2020
Community detection is a critical issue in the field of complex networks. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks.
Hong Lu, Xiaoshuang Sang, Qinghua Zhao, Jianfeng Lu   +3 more
doaj   +1 more source

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza   +5 more
doaj   +1 more source

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Big Data Mining and Analytics
Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.
Guosheng Cui, Ye Li, Jianzhong Li, Jianping Fan   +3 more
doaj   +1 more source

Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection

Complexity, 2019
Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of ...
Yong-Jing Hao, Ying-Lian Gao, Mi-Xiao Hou, Ling-Yun Dai, Jin-Xing Liu   +4 more
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

Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation

Complexity, 2018
Obtaining an optimum data representation is a challenging issue that arises in many intellectual data processing techniques such as data mining, pattern recognition, and gene clustering.
Wei Jiang, Qian Lv, Chenggang Yan, Kewei Tang, Jie Zhang   +4 more
doaj   +1 more source

Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

IEEE Access, 2019
Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields.
Lin Zhang, Zhonghua Liu, Lin Wang, Jiexin Pu   +3 more
doaj   +1 more source

A flexible R package for nonnegative matrix factorization

BMC Bioinformatics, 2010
Background Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have
Seoighe Cathal, Gaujoux Renaud
doaj   +1 more source

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances.
E. M. M. B. Ekanayake, H. M. H. K. Weerasooriya, D. Y. L. Ranasinghe, S. Herath, B. Rathnayake, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, H. M. V. R. Herath   +7 more
doaj   +1 more source