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Showing 1 to 19 of 19 for “"Nonnegative matrix factorization"”.
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Nonnegative Matrix Factorization and Document Classification
<p>Applications of Non-negative Matrix Factorization are ubiquitous, and there are several well known algorithms available. This paper is concerned with the preprocessing of the documents and how the preprocessing effects document classification. The preprocessing discussed in this paper will run …
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Speech denoising using nonnegative matrix factorization and neural networks
… methods were mainly adopted for this purpose, nonnegative matrix factorization (NMF) and neural networks. Experiments were conducted to compare the performance of these two methods for speech denoising. For each of these methods, we compared the performance of the case where we had prior …
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Advances in nonnegative matrix factorization with application on data clustering.
… representation of high-dimensional data, based nonnegative matrix factorization (NMF) framework, for better clustering. Specifically, there are three methods as follows: - Multiple Components Based Representation Learning Real data are usually complex and contain various components. For example, …
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Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization
Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) are widely used in the physical sciences; this thesis explores the often-overlooked origins of NMF in the psychometrics literature. Another method originating in psychometrics is sequentially-fit …
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An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony
… of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation …
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Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization
… least squares (NNLS) problem, and the nonnegative matrix factorization (NMF) problem. In this paper, a method to solve the NNLS problem in an adaptive way is discussed. Additionally, possible ways to apply this, and other related method, to adaptive NMF problems are discussed.
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Pattern extraction and clustering for high-dimensional discrete data
We explore connections of low-rank matrix factorizations with interesting problems in data mining and machine learning. We propose a framework for solving several low-rank matrix factorization problems, including binary matrix factorization, constrained binary matrix factorization, weighted …
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Matrix Factorization for Learning Metagenomic Pathways and Species
… from metagenomic data. The methods are based on Nonnegative Matrix Factorization (NMF). The rows of our data matrix correspond to metagenomic samples and columns correspond to chemical reactions present in the samples. In order to learn both pathways and OTUs as well as relationships between …
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Identifying drug-target and drug-disease associations using computational intelligence
… MDIPA, NMTF-DTI, and NTD-DR. MDIPA is a nonnegative matrix factorization-based method to predict interaction scores of drug-microRNA pairs, where the interaction scores can effectively be used for drug repositioning. This method uses the functional similarity of microRNAs and structural …
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Two new approaches for learning Hidden Markov Models
… HMMs. This thesis takes Angluin's approach and nonnegative matrix factorization and applies them to learning HMMs. Angluin's approach fails and the reasons are discussed. The matrix factorization approach is successful, allowing us to produce a novel method of learning HMMs. The new method is …
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Statistical Learning with Discrete Structures: Statistical and Computational Perspectives
… fifth chapter, we study the problem of sparse Nonnegative Matrix Factorization (NMF) using Cutler and Breiman's archetypal regularization. We explore the utility of our methods in the context of applications in the biomedical sciences, computer vision and computational finance.
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Quadratic maximization under combinatorial constraints and related applications
… solve the maximization exactly when the argument matrix of the quadratic objective is positive semidefinite and has constant rank. Our approach relies on a hyper-spherical transformation of the low-rank space and has complexity that scales exponentially in the rank of the input, but polynomially …
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Comparison of muscle synergies elicited from transcranial meganetic stimulation (tms) and voluntary movements
… demonstrated that muscle synergies decomposed by Nonnegative Matrix Factorization (NMF) from EMG patterns evoked by intra-cortical microsimulation (ICMS) in the monkey remarkably matched ones observed in naturalistic reach-and-grasp behaviors. Another study (Ajiboye et al. 2009) showed that …
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Bayesian nonparametric learning for complicated text mining
… First, three Bayesian nonparametric sparse nonnegative matrix factorization models, based on two innovative dependent Indian buffet processes, are proposed for document-word co-clustering tasks. Second, a Dirichlet mixture probability measure strategy is proposed to link the topics from …
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Exploiting spatial and spectral information for audio source separation and speaker diarization
… In the first part, spectral modeling based on Nonnegative Matrix Factorization is adopted to represent the source signals. The parameters of Gaussian model-based source separation are estimated in sense of Maximum-Likelihood using a Generalized Expectation-Maximization algorithm by applying …
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Chemical identification under a poisson model for Raman spectroscopy
… known reference library. A novel variation of nonnegative matrix factorization (NMF) is developed to address this problem. Our simulations indicate that this algorithm gives better estimation performance than the standard two-stage NMF approach and the fully supervised approach when there are …
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Algorithmic advances in learning from large dimensional matrices and scientific data
… from linear algebra and approximation theory for matrix spectrum related problems such as numerical rank estimation, matrix function trace estimation including log-determinants, Schatten norms, and other spectral sums. We also propose a new method which simultaneously estimates the dimension of …
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Unsupervised feature analysis for high dimensional big data
… image local learning regularized orthogonal nonnegative matrix factorization is used to learn pseudo labels and simultaneously robust joint $l_{2,1}$-norm minimization is performed to select discriminative features. Cross-view consensus on pseudo labels can be obtained as much as possible. …
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Phase difference and tensor factorization models for audio source separation
Made available in DSpace on 2017-03-01T15:46:01Z (GMT). No. of bitstreams: 2 TRAA-DISSERTATION-2016.pdf: 12461339 bytes, checksum: aeab068ca641be012f3b6c8b1f19d354 (MD5) LICENSE.txt: 4210 bytes, checksum: 63ac2ac06dfd739a5099ea28ac00e136 (MD5) Previous issue date: 2016-10-10