Results 301 to 310 of about 142,276 (359)

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

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

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

Non-Negative Matrix Factorization With Locality Constrained Adaptive Graph

IEEE transactions on circuits and systems for video technology (Print), 2020
Non-negative matrix factorization (NMF) has recently attracted much attention due to its good interpretation in perception science and widely applications in various fields.
Yugen Yi, Jianzhong Wang, Wei Zhou, Caixia Zheng, Jun Kong, Shaojie Qiao   +5 more
semanticscholar   +1 more source

Autofluorescence Removal by Non-Negative Matrix Factorization

IEEE Transactions on Image Processing, 2011
This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is
Franco, Woolfe, Michael, Gerdes, Musodiq, Bello, Xiaodong, Tao, Ali, Can   +4 more
openaire   +2 more sources

Non-negative matrix factorization

2017
Non-negative matrix factorization - NMF is a Linear Dimensionality Reduction method, which approximates a high dimensional non-negative data matrix by a multiplica- tion of two low-ranked matrices that preserves the non-negativity of the data. This property has proven to be beneficial as it allows for the approximated data to be interpreted in the same
openaire   +1 more source

Novel Algorithm for Non-Negative Matrix Factorization

New Mathematics and Natural Computation, 2015
Non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with non-negative constraints. This problem attracts much attention from researchers for theoretical reasons and for potential applications.
Tran Dang Hien, Do Van Tuan, Pham Van At, Le Hung Son   +3 more
openaire   +2 more sources

Email Surveillance Using Non-negative Matrix Factorization

Computational and Mathematical Organization Theory, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Berry, Michael W., Browne, Murray
openaire   +1 more source

Non-Negative Matrix Factorization With Dual Constraints for Image Clustering

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020
How to learn dimension-reduced representations of image data for clustering has been attracting much attention. Motivated by that the clustering accuracy is affected by both the prior-known label information of some of the images and the sparsity feature
Zuyuan Yang, Yu Zhang, Yong Xiang, Wei Yan, Shengli Xie   +4 more
semanticscholar   +1 more source

Rank-Adaptive Non-Negative Matrix Factorization

Cognitive Computation, 2018
Dimension reduction is a challenge task in data processing, especially in high-dimensional data processing area. Non-negative matrix factorization (NMF), as a classical dimension reduction method, has a contribution to the parts-based representation for the characteristics of non-negative constraints in the NMF algorithm.
Dong Shan, Xinzheng Xu, Tianming Liang, Shifei Ding   +3 more
openaire   +1 more source