Results 11 to 20 of about 112,822 (316)
Shifted Non-Negative Matrix Factorization [PDF]
2007 IEEE Workshop on Machine Learning for Signal Processing, 2007 Non-negative matrix factorization (NMF) has become a widely used blind source separation technique due to its part based representation and ease of interpretability. We currently extend the NMF model to allow for delays between sources and sensors. This is a natural extension for spectrometry data where a shift in onset of frequency profile can be ...
Lars Kai Hansen +2 more
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NeuroImage, 2020 In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to ...
Raihaan Patel +7 more
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Blind source separation with optimal transport non-negative matrix factorization
EURASIP Journal on Advances in Signal Processing, 2018 Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF ...
Antoine Rolet +3 more
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Topic supervised non-negative matrix factorization [PDF]
, 2017 Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation.
MacMillan, K., Wilson, James D
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Non-negative matrix factorization with sparseness constraints
Journal of Machine Learning Research, 2004 Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations.
Patrik O. Hoyer, Peter Dayan
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Musical instrument classification using non-negative matrix factorization algorithms [PDF]
, 2006 In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 ...
Emmanouil Benetos +2 more
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Feature Weighted Non-Negative Matrix Factorization [PDF]
IEEE Transactions on Cybernetics, 2023 Non-negative Matrix Factorization (NMF) is one of the most popular techniques for data representation and clustering, and has been widely used in machine learning and data analysis. NMF concentrates the features of each sample into a vector, and approximates it by the linear combination of basis vectors, such that the low-dimensional representations ...
Mulin Chen, Maoguo Gong, Xuelong Li
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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]
ITM Web of Conferences, 2022 —What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
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Accounting for phase cancellations in non-negative matrix factorization using weighted distances [PDF]
, 2014 (c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Sebastian Ewert +2 more
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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.
Shu-Zhen Lai, Hou‐Biao Li, Zutao Zhang
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