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Showing 1 to 20 of 39 for “"Non-negative Matrix Factorization"”.
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Online parameter selection for source separation using non-negative matrix factorization
… source separation algorithm which can handle non-stationary noises. To address the implementation, we also present a study on how to select the parameters in the separation algorithm in order to deliver the best performance for denoising using a statistical metric we have defined.
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Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization
Problems with non-negativity constrains have recently attracted a great deal of interest. Non-negativity constraints arise naturally in many applications, and are often necessary for proper interpretation. Furthermore, these constrains provide an intrinsic sparsity that may be of value in certain …
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Avoiding broadened and negative peaks in non-negative matrix factorization: Thermal expansion and background corrections for in-situ diffraction data
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning
… collection of solutions in specific models. Non-negative Matrix Factorization (NMF) is a popular data exploration tool and its Bayesian formulation is a promising approach for understanding uncertainty within this structure. We demonstrate that current approaches are lacking in the proper …
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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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Uma investigação sobre métodos de separação cega de fontes sonoras envolvendo representações não-negativas e diversidade espacial
… this problem for sound sources, employing non-negative signals’ representations, while also taking advantage of the spatial diversity induced by the use of multiple channels; this particular feature has recently opened up new research directions regarding the proper modeling of multichannel …
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Missing values imputation and image registration for genetics applications
… optimal recovery and derive an error bound for non-negative matrix factorization of the imputed data. Second, we consider missing values as erasure channels and show examples of using Fano's inequality to find lower bounds on missing values algorithms. Finally, we perform image registration of …
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Learning with matrix factorizations
… or high-dimensional data. Models based on matrix factorization (Factor Analysis, PCA) have been extensively used in statistical analysis and machine learning for over a century, with many new formulations and models suggested in recent years (Latent Semantic Indexing, Aspect Models, …
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Towards parallax-based unencumbered displays
… barrier masks are produced by decomposing a matrix, which is created by applying a set of constraints to the input light field, using non-negative matrix factorization. We compare a number of matrix factorization methods, including a novel technique developed in this work. We provide a …
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EMG-EMG Coherence Analysis of Elbow and Shoulder Muscles
… in muscle pairs was discovered through a non-negative matrix factorization: NMF) analysis. In order to evaluate the same muscles under the frequency domain, coherence analysis: a correlational method) was used. Additionally, a comparison can be made to determine if the resulting muscle …
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End-to-end non-negative auto-encoders: a deep neural alternative to non-negative audio modeling
Over the last decade, non-negative matrix factorization (NMF) has emerged as one of the most popular approaches to modeling audio signals. NMF allows us to factorize the magnitude spectrogram to learn representative spectral bases that can be used for a wide range of applications. With the recent …
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Sensory modulation of muscle synergies for motor adaptation during natural behaviors
… combining a few synergies, identified by the non-negative matrix factorization algorithm. But to what extent synergies are neural constraints, or merely structures reflecting experimental constraints, has remained an open question. I address this question with the hypothesis that, muscle …
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Data-Driven Insights into Spatial Patterns and Disease Etiologies of White Matter Hyperintensities
… thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific …
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Optimization algorithms for inference and classification of genetic profiles from undersampled measurements
… profiles. First, we extend the deterministic Non-negative Matrix Factorization (NMF) framework to the probabilistic case (PNMF). We apply the PNMF algorithm to cluster and classify DNA microarrays data. The proposed PNMF is shown to outperform the deterministic NMF and the sparse NMF …
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Data Clustering And Visualization Through Matrix Factorization
… this dissertation is two-fold: Semi-Supervised Non-negative Matrix Factorization (SS-NMF) for data clustering/co-clustering and Exemplar-based data Visualization (EV) through matrix factorization. Compared to traditional data mining models,</p> <p>matrix-based methods are fast, easy to …
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Dimensionality reduction in the control of quasi-static force production tasks in humans
… explored: principal component analysis (PCA) and non-negative matrix factorization (NMF) subjected to a generalized Akaike information criterion (AIC) to serve as a quality of fit estimator. These are used to group the muscle activity of individuals during quasi-static force production tasks to …
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EMG methods for prosthesis ankle-subtalar free-space control
… solve those problems respectively. They are 1. A non-negative blind source separation algorithm named non-negative orthogonal decomposition(NOD). This algorithm aims to replace non-negative matrix factorization(NMF) for muscle motion base extraction. NOD recovers the source signal by finding the …
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Methods for multiple pitch tracking and instrument separation from monaural polyphonic recordings
… with each other. Although audio sources are non-stationary, their spectra have a considerable amount of structure that can differentiate them from other sources. Recently non-negative matrix factorization (NMF) and probabilistic latent component analysis (PLCA) have been used by many …
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Single-particle instrument simulator: Bridging experiments and models
… ratios. Since ion signals depend not only non-linearly on species mass but also on species-specific ionization efficiencies, fragmentation patterns, and overlapping signals, there is no straightforward mapping between mass spectra and model species masses. To bridge this gap, we develop …
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Machine learning methodologies for high dimensional biomedical & bioinformatics applications
… in these applications, novels methods in matrix factorization, image registration, and deep learning are proposed. For the first project on text mining, we propose the semi-orthogonal non-negative matrix factorization as a topic model to investigate the potential of using triage notes to …
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