Results 291 to 300 of about 112,822 (316)

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
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Non-negative matrix factorization with α-divergence

Pattern Recognition Letters, 2008
Non-negative matrix factorization (NMF) is a popular technique for pattern recognition, data analysis, and dimensionality reduction, the goal of which is to decompose non-negative data matrix X into a product of basis matrix A and encoding variable matrix S with both A and S allowed to have only non-negative elements. In this paper, we consider Amari's
Cichocki, A, Lee, H, Kim, YD, Choi, S
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Image prediction based on non-negative matrix factorization

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative texture prediction methods have recently been considered for image prediction.
Turkan, Mehmet, Guillemot, Christine
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Optimum Factorization of Non-Negative Matrix Functions

Theory of Probability & Its Applications, 1964
The proposition about optimum factorization of a non-negative matrix function $f(\lambda )$ is generalized for the case where the unknown function $A(z)$ of class $H_2 $ satisfies the inequality \[ A\left( {e^{ - i\lambda } } \right)A^ * \left( {e^{ - i\lambda } } \right) \leqq 2\pi f(\lambda ) \] instead of the usual equality \[ A\left( {e^{ - i ...
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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.
Tianming Liang, Dong Shan, Xinzheng Xu, Shifei Ding   +3 more
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Convex Non-negative Matrix Factorization in the Wild

2009 Ninth IEEE International Conference on Data Mining, 2009
Non-negative matrix factorization (NMF) has recently received a lot of attention in data mining, information retrieval, and computer vision. It factorizes a non-negative input matrix V into two non-negative matrix factors V = WH such that W describes "clusters" of the datasets.
Christian Bauckhage, Christian Thurau, Kristian Kersting   +2 more
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Non-negative Matrix Factorization for Face Recognition

2002
The computer vision problem of face classification under several ambient and unfavorable conditions is considered in this study. Changes in expression, different lighting conditions and occlusions are the relevant factors that are studied in this present contribution.
David Guillamet, Jordi Vitrià
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The biofilm matrix: multitasking in a shared space

Nature Reviews Microbiology, 2022
Hans-Curt Flemming, Eric D Van Hullebusch, Thomas R Neu   +2 more
exaly  

Extracellular vesicle–matrix interactions

Nature Reviews Materials, 2023
, Jae-won Shin
exaly  

The matrix in cancer

Nature Reviews Cancer, 2021
Thomas Cox
exaly