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Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014
Non-negative matrix factorization (NMF) is a well known method for obtaining low rank approximations of data sets, which can then be used for efficient indexing, classification, and retrieval. The non-negativity constraints enable probabilistic interpretation of the results and discovery of generative models.
Xilun Chen, K. Selçuk Candan
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Non-negative matrix factorization (NMF) is a well known method for obtaining low rank approximations of data sets, which can then be used for efficient indexing, classification, and retrieval. The non-negativity constraints enable probabilistic interpretation of the results and discovery of generative models.
Xilun Chen, K. Selçuk Candan
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IEEE journal of biomedical and health informatics, 2020
Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that.
Cui-Na Jiao +4 more
semanticscholar +1 more source
Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that.
Cui-Na Jiao +4 more
semanticscholar +1 more source
Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus
, 2020Topic modeling is a method for finding abstract topics in a large collection of documents With it, it is possible to discover the mixture of hidden or “latent” topics that varies from document to document in a given corpus As an unsupervised machine ...
S. Mifrah, E. Benlahmar
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Kinetic study on the slow pyrolysis of nonmetal fraction of waste printed circuit boards (NMF-WPCBs)
Waste Management Research, 2020In this study, the pyrolysis behaviour of nonmetal fraction of waste printed circuit boards (NMF-WPCBs) was studied based on five model-free methods and distributed activation energy model (DAEM).
Z. Yao +6 more
semanticscholar +1 more source
Sensor nodes fault detection for agricultural wireless sensor networks based on NMF
Computers and Electronics in Agriculture, 2019Nowadays, Wireless Sensor Networks (WSN) are widely been employed to solve agricultural problems related to the optimization of scarce farming resources, decision making support, and land monitoring.
Jimmy Ludeña-Choez +1 more
exaly +2 more sources
Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2019 In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial ...
Sean U N Wood, Jean Rouat
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Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006
In this paper, we show that PLSI and NMF optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. In addition, we also propose a new hybrid method that runs PLSI and NMF alternatively to achieve better solutions.
Chris Ding, Tao Li, Wei Peng
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In this paper, we show that PLSI and NMF optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. In addition, we also propose a new hybrid method that runs PLSI and NMF alternatively to achieve better solutions.
Chris Ding, Tao Li, Wei Peng
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NMF Based System for Speaker Identification
2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), 2021Automatic identification of speakers from text-independent information is a task required in a broad base of applications. Gaussian Mixture Models are a state-of-the-art solution to the task. We apply this method to a text-independent speech dataset, and present a novel method using Nonnegative Matrix Factorization and sparseness constraints.
Costantini G., Cesarini V., Paolizzo F.
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Topographic NMF for Data Representation
IEEE Transactions on Cybernetics, 2014Nonnegative matrix factorization (NMF) is a useful technique to explore a parts-based representation by decomposing the original data matrix into a few parts-based basis vectors and encodings with nonnegative constraints. It has been widely used in image processing and pattern recognition tasks due to its psychological and physiological interpretation ...
Xiao, Yanhui +5 more
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2008 IEEE Southwest Symposium on Image Analysis and Interpretation, 2008
Non-negative matrix factorization (NMF) has increasingly been used for efficiently decomposing multivariate data into a signal dictionary and corresponding activations. In this paper, we propose an algorithm called sparse shift-invariant NMF (ssiNMF) for learning possibly overcomplete shift- invariant features. This is done by incorporating a circulant
Vamsi K. Potluru +2 more
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Non-negative matrix factorization (NMF) has increasingly been used for efficiently decomposing multivariate data into a signal dictionary and corresponding activations. In this paper, we propose an algorithm called sparse shift-invariant NMF (ssiNMF) for learning possibly overcomplete shift- invariant features. This is done by incorporating a circulant
Vamsi K. Potluru +2 more
openaire +1 more source