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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
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2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply.
J. Eggert, E. Korner
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Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply.
J. Eggert, E. Korner
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2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2016
In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features.
Viet-Hang Duong +4 more
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In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features.
Viet-Hang Duong +4 more
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From Binary NMF to Variational Bayes NMF: A Probabilistic Approach
2015A survey of our recent work on probabilistic NMF is provided. All variants discussed here are illustrated by their application to the analysis of failure patterns emerging from manufacturing and processing silicon wafers. It starts with binNMF, a variant developed to apply NMF to binary data sets. The latter are modeled as a probabilistic superposition
R. Schachtner +3 more
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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 +2 more
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Cauchy sparse NMF with manifold regularization: A robust method for hyperspectral unmixing
Knowledge-Based Systems, 2019Recently, nonnegative matrix factorization (NMF) has achieved a great success in hyperspectral image (HSI) unmixing tasks. However, existing NMF based unmixing methods commonly suffer from two main drawbacks: 1) the lack of robustness, which leads to the
Haotian Wang, Wenjing Yang, Naiyang Guan
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Cauchy NMF for Hyperspectral Unmixing
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020Non-negative matrix factorization (NMF) is a classical hyperspectral unmixing model which minimizes the Euclidean distance between the hyperspectral data matrix and its low rank approximation (i.e., the product of endmember matrix and abundance matrix), and it fails when applied to noisy data because the loss function is sensitive to outliers.
Jiangtao Peng +3 more
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