Nonnegative Matrix Factorization
Nonnegative Matrix Factorization with Zellner Penalty
... Zellner nonnegative matrix factorization ...Constrained nonnegative matrix factorization (CNMF) [8] and show that it is superior across all selected factorization ...
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Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
... Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representa- ...the ...
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A Robust Symmetric Nonnegative Matrix Factorization Framework for Clustering Multiple Heterogeneous Microbiome Data
... In this paper, we introduce a novel framework (RSNMF) for data integration based on similarity network fusion (SNF) and symmetric nonnegative matrix factorization (SNMF). We extend the similarity ...
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Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
... We present a numerical algorithm for nonnegative matrix factorization (NMF) problems under noisy separability. An NMF problem under separability can be stated as one of finding all vertices of the ...
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Ranking Preserving Nonnegative Matrix Factorization
... Nonnegative matrix factorization (NMF), a well- known technique to find parts-based representa- tions of nonnegative data, has been widely ...reserving nonnegative matrix ...
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A Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
... Nonnegative matrix factorization (NMF) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering ...
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Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
... Abundant LMM unmixing algorithms have been proposed. Some of these focus on the endmember extraction from statis- tical and geometrical aspects, such as Pixel Purity Index [13], N-FINDR [14], alternating projected ...
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Robust Multi Relational Clustering via ℓ1 Norm Symmetric Nonnegative Matrix Factorization
... Traditional clustering aims to partition data points into several groups, such that the data points in the same group can share some commonalities whilst those from different groups are dissimilar. With the recent ...
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A multilevel approach for nonnegative matrix factorization
... Nonnegative matrix factorization (NMF), the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices, has been shown to be useful in ...
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A study of blind source separation using nonnegative matrix factorization
... APPLIED INACOUSTIC SOURCE SEPARATION For the acoustic source separation, the conventional multichannel blind source separation algorithm was performing well in multichannel real-world recording data. However, the ...
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Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization
... supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion ...
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Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers
... Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available ...
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Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field
... As we know, there are several approaches that have been developed to solve the BSS problem such as the supervised Independent Component Analysis (ICA) (Y. Li et al., 2006; Jang and Lee, 2003; Fevotte and Godsill, 2006) ...
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Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
... Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, under which all the columns of the input data matrix be- long to the convex ...
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On rationality of nonnegative matrix factorization
... Nonnegative matrix factorization (NMF) is the task of factoring a matrix of nonnegative real numbers M (henceforth a nonnegative matrix) as a product M = W · H such that ...
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Jacobi Algorithm for Nonnegative Matrix Factorization with Transform Learning
... —Nonnegative matrix factorization (NMF) is the state- of-the-art approach to unsupervised audio source ...the factorization of a given short-time frequency transform into a dictionary of ...
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Area Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
... To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix ...
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Multi-task sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising
... Hyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, among many methods dedicated to ...
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Quantized nonnegative matrix factorization
... though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission ...tized ...
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Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization
... about nonnegative matrix factorization (NMF) neglect to give an accurate account of the origins of the ...“Nonnegative Matrix Factorization“ was even ...
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