Results 291 to 300 of about 135,346 (336)

Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization

IEEE Transactions on Network Science and Engineering, 2021
Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle
Xin Luo, Zhigang Liu, Mingsheng Shang, Jungang Lou, Mengchu Zhou   +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

Robust non-negative matrix factorization [PDF]

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.
Zhengguang Chen, Lijun Zhang, Xiaofei He, Miao Zheng   +3 more
openaire   +1 more source

Uniqueness of Non-Negative Matrix Factorization [PDF]

2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007
In this paper, two new properties of stochastic vectors are introduced and a strong uniqueness theorem on non-negative matrix factorizations (NMF) is introduced. It is described how the theorem can be applied to two of the common application areas of NMF, namely music analysis and probabilistic latent semantic analysis. Additionally, the theorem can be
openaire   +2 more sources

Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†

, 1994
A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m.
P. Paatero, U. Tapper
semanticscholar   +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

Blind source separation of molecular components of the human skin in vivo: non-negative matrix factorization of Raman microspectroscopy data.

In Analysis, 2021
Determination of the molecular composition of the skin is crucial for numerous tasks in medicine, pharmacology, dermatology and cosmetology. Confocal Raman microspectroscopy is a sensitive method for the evaluation of molecular depth profiles in the skin
B. P. Yakimov, A. V. Venets, J. Schleusener, V. Fadeev, J. Lademann, E. Shirshin, M. Darvin   +6 more
semanticscholar   +1 more source

Semi-Supervised Non-Negative Matrix Factorization With Dissimilarity and Similarity Regularization

IEEE Transactions on Neural Networks and Learning Systems, 2020
In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model by means of elegantly modeling the label information. The proposed model is capable of generating discriminable low-dimensional representations to improve ...
Yuheng Jia, S. Kwong, Junhui Hou, Wenhui Wu   +3 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.
Jie Liu, Yuan Wang, Caihua Liu, Zhicheng He, Airu Yin, Yalou Huang   +5 more
openaire   +2 more sources

Convex Non-Negative Matrix Factorization With Adaptive Graph for Unsupervised Feature Selection

IEEE Transactions on Cybernetics, 2020
Unsupervised feature selection (UFS) aims to remove the redundant information and select the most representative feature subset from the original data, so it occupies a core position for high-dimensional data preprocessing.
Aihong Yuan, Mengbo You, Dongjian He, Xuelong Li   +3 more
semanticscholar   +1 more source