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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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NMF-Based Image Quality Assessment Using Extreme Learning Machine
IEEE Transactions on Cybernetics, 2017Chenwei Deng, Weisi Lin, Guang-Bin Huang
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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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Deep Learning Based Speech Separation via NMF-Style Reconstructions
IEEE/ACM Transactions on Audio Speech and Language Processing, 2018Deep learning based speech separation usually uses a supervised algorithm to learn a mapping function from noisy features to separation targets. These separation targets, either ideal masks or magnitude spectrograms, have prominent spectro-temporal ...
Shuai Nie, Shan Liang, Wenju Liu
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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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Constrained NMF-based semi-supervised learning for social media spammer detection
Knowledge-Based Systems, 2017Bin Fu
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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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2003
In this paper, we propose a font classification method in scanned documents using non-negative matrix factorization (NMF). Using NMF, we automatically extract spatially local features enough to classify each font. The appropriateness of the features to classify a specific font is shown in the experimental results.
Chang Woo Lee +3 more
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In this paper, we propose a font classification method in scanned documents using non-negative matrix factorization (NMF). Using NMF, we automatically extract spatially local features enough to classify each font. The appropriateness of the features to classify a specific font is shown in the experimental results.
Chang Woo Lee +3 more
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Sparse dual graph-regularized NMF for image co-clustering
Neurocomputing, 2018Nonnegative matrix factorization (NMF) as fundamental technique for clustering has been receiving more and more attention. This is because it can effectively reduce high dimensional data and produce parts-based, linear image representations of ...
Jing Sun +3 more
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