Results 31 to 40 of about 7,782 (177)

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

A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization [PDF]

, 2013
As we all known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing and signal processing etc.
Lai, Shu-Zhen, Li, Hou-Biao, Zhang, Zu-Tao   +2 more
core   +3 more sources

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

Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices [PDF]

, 2013
Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of
Gillis, Nicolas
core   +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

A deep matrix factorization method for learning attribute representations [PDF]

, 2015
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix
Bousmalis, Konstantinos, Schuller, Bjoern W., Trigeorgis, George, Zafeiriou, Stefanos   +3 more
core   +3 more sources

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

Generalized Separable Nonnegative Matrix Factorization

, 2019
Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing.
Gillis, Nicolas, Pan, Junjun
core   +1 more source

Randomized Nonnegative Matrix Factorization

, 2018
Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin, Kutz, J. Nathan, Mendible, Ariana, Wihlborn, Sophie   +3 more
core   +1 more source