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Showing 1 to 20 of 127 for “"Matrix factorization"”.
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Matrix Factorization: Nonnegativity, Sparsity and Independence
Matrix factorization arises in a wide range of application domains and is useful for extracting the latent features in the dataset. Examples include recommender systems, brain data analysis, and document clustering. In this dissertation, we are interested in matrix factorizations which impose the …
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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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Predicting NBA games with matrix factorization
… the methods I use to predict NBA games using matrix factorization. Matrix factorization is popular through the Netflix recommendation problem, but in general, one can apply it to data that are best modeled as the result of pairwise interaction. My thesis contains three parts. First, I explain …
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Data Clustering And Visualization Through Matrix Factorization
… 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 understand and …
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Convex matrix factorization for gene expression analysis
… for gene expression analysis relying upon convex matrix factorization (CMF). In CMF, one of the matrix factors has a convexity constraint, that is, each row is nonnegative and sums to one, and hence can be interpreted as a probability distribution. This is motivated biologically by expression data …
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Matrix Factorization for Learning Metagenomic Pathways and Species
… 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 them, we …
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Speech denoising using nonnegative matrix factorization and neural networks
… 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 information of …
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Bayesian and Positive Matrix Factorization approaches to pollution source apportionment
The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and …
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Advances in nonnegative matrix factorization with application on data clustering.
… 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, face images …
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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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Online parameter selection for source separation using non-negative matrix factorization
Blind source separation has been an area of study recently due to the many applications that might benefit from a good blind source separation algo- rithm. One instance is using blind source separation for audio denoising in cellular phones. In almost all instances, we have very little, if any, …
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Combinatorial aspects of low-rank matrix factorization and two applications in bioinformatics
… mining applications we need to write a given matrix Y as a low-rank product Y = AX. Both matrices A and X have to be determined and we assume that from the specifics of the application we can derive some constraints for A and X. In general, there are different factorizations that approximate a …
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Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization
… 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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Evaluating sources of volatile organic compounds in Colorado workplaces via positive matrix factorization
… 61 target VOCs via EPA Method TO-15. Positive Matrix Factorization (PMF) modeling was employed to identify and apportion the sources of VOCs, providing insights into the relative contributions of indoor and outdoor pollutants. The findings inform further understanding of patterns of indoor VOCs …
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FedSmarteum: Secure Federated Matrix Factorization Using Smart Contracts for Multi-Cloud Supply Chain
… this, we build a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update localized items vectors. Furthermore, we utilize Homomorphic …
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New optimization approaches to matrix factorization problems with connections to natural language processing
… novel formulation optimization methods for four matrix factorization problems in depth: sparse principal component analysis, compressed sensing, discrete component analysis, and latent Dirichlet allocation. For each new formulations, we develop efficient solution algorithms using discrete and …
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A structured matrix factorization method for computational modeling of hierarchical polarization in social interactions
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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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
… 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 characterization …
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