Results 241 to 250 of about 17,677 (287)
The B7 family subgroup reflects tumor cell heterogeneity and patient post-operative prognosis in gallbladder cancer. [PDF]
Biol DirectMa C +10 more
europepmc +1 more source
Resolving ionic spectra of lead-halide perovskites to the nanometer.
NanoscaleĆavar LD +6 more
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Shifted NMF with Group Sparsity for Clustering NMF Basis Functions [PDF]
, 2012 Recently, Non-negative Matrix Factorisation (NMF) has found application in separation of individual sound sources. NMF decomposes the spectrogram of an audio mixture into an additive parts based representation where the parts typically correspond to individual notes or chords. However, there is a need to cluster the NMF basis functions to their sources.
Jaiswal, Rajesh +3 more
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, 2015 Nowadays, magnetic resonance spectroscopy (MRS) represents a powerful nuclear magnetic resonance (NMR) technique in oncology since it provides information on the biochemical profile of tissues, thereby allowing clinicians and radiologists to identify in a non-invasive way the different tissue types characterising the sample under investigation.
T. Laudadio +5 more
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Leveraging Joint-Diagonalization in Transform-Learning NMF
IEEE Transactions on Signal Processing, 2022 International audienceNon-negative matrix factorization with transform learning (TL-NMF) is a recent idea that aims at learning data representations suited to NMF. In this work, we relate TL-NMF to the classical matrix joint-diagonalization (JD) problem.
Sixin Zhang +2 more
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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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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.
Julian Eggert, Edgar Körner
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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.
Julian Eggert, Edgar Körner
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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 H. Q. Ding +2 more
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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 H. Q. Ding +2 more
openaire +1 more source
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 ...
Yanhui Xiao +5 more
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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
openaire +1 more source