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Non-negative Matrix Factorization on Kernels
2006In this paper, we extend the original non-negative matrix factorization (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with data where only relationships (similarities or dissimilarities) between objects ...
Zhi-Hua Zhou +2 more
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Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection
IEEE Transactions on Automation Science and EngineeringCommunity is a fundamental and highly desired pattern in a Large-scale Undirected Network (LUN). Community detection is a vital issue when LUN representation learning is performed.
Zhigang Liu, Xin Luo, Mengchu Zhou
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Non-Negative Matrix Factorization With Locality Constrained Adaptive Graph
IEEE transactions on circuits and systems for video technology (Print), 2020Non-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 +5 more
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Non-negative matrix factorization for EEG
2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013Today with the progress of science and technology becomes signal analysis, data analysis and data mining are very Important in most science and engineering applications. Extracting useful knowledge from experimental raw datasets, measurements, observations and analysis and understand complex data has become very important challenge in the world.
Ibrahim Salem Jahan, Vaclav Snasel
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Semisupervised Adaptive Symmetric Non-Negative Matrix Factorization
IEEE Transactions on Cybernetics, 2020As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can generate the clustering result without additional post-processing, by decomposing a similarity matrix into the product of a clustering indicator matrix and its transpose.
Yuheng Jia +3 more
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Non-Negative Matrix Factorization With Dual Constraints for Image Clustering
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020How 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 +4 more
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Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration
IEEE/CAA Journal of Automatica Sinica, 2021This paper presents a novel medical image registration algorithm named total variation constrained graph-regularization for non-negative matrix factorization (TV-GNMF).
Chengcai Leng +4 more
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Multiobjective Sparse Non-Negative Matrix Factorization
IEEE Transactions on Cybernetics, 2019Non-negative matrix factorization (NMF) is becoming increasingly popular in many research fields due to its particular properties of semantic interpretability and part-based representation. Sparseness constraints are usually imposed on the NMF problems in order to achieve potential features and sparse representation.
Maoguo Gong +3 more
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Autofluorescence Removal by Non-Negative Matrix Factorization
IEEE Transactions on Image Processing, 2011This 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
Ali Can +4 more
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When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Neural Information Processing Systems, 2003We interpret non-negative matrix factorization geometrically, as the problem of finding a simplicial cone which contains a cloud of data points and which is contained in the positive orthant.
D. Donoho, V. Stodden
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