Results 61 to 70 of about 20,683 (150)
, 2023 openIn this thesis, we present an innovative approach for topic modeling and text classification using a combination of Non-Negative Matrix Factorization (NMF), Variational Autoencoder (VAE), and Bidirectional Long Short-Term Memory (Bi-LSTM) models. Our
JAVIDFAR, MASOUD
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Multichannel high resolution NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain [PDF]
, 2013 Badeau, Roland, Plumbley, Mark
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Remote Sensing, 2018 Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials.
Charlotte Revel +4 more
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Comparison of Standard-NMF and Full-NMF over the DLBCL data of Data set 1.
, 2013 The matrix produced by using the DLBCL samples of Data set 1 as rows and their DNA copy number profiles as columns was used to test the distinct manners of running NMF. Full-NMF stands for the procedure which runs over all data, while Standard-NMF stands
Emanuele Zucca (487408) +5 more
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IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
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Another Robust NMF: Rethinking the Hyperbolic Tangent Function and Locality Constraint
IEEE Access, 2019 Non-negative matrix factorization (NMF) is a classical data analysis tool for clustering tasks. It usually considers the squared loss to measure the reconstruction error, thus it is sensitive to the presence of outliers. Looking into the literature, most
Xingyu Shen +4 more
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Using NMF to decompose the NMF-based matrix from the patient–disease diagnosis matrix.
, 2018 Using NMF to decompose the NMF-based matrix from the patient–disease diagnosis matrix.
Vincent S. Tseng (181095) +2 more
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Comparison of Compact-NMF and Full-NMF over the DLBCL data of Data set 1.
, 2013 The matrix produced by using the DLBCL samples of Data set 1 as rows and their DNA copy number profiles as columns was used to test the distinct manners of running NMF.
Emanuele Zucca (487408) +5 more
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, 2011 In this paper, we propose a maximum-margin framework for classification using Non-negative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization are separated, we incorporate the maximum margin ...
Patras, I., Kotsia, I., Kumar, B.
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