Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 48 for “"Low-rank matrix"”.
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Low rank matrix completion
We consider the problem of recovering a low rank matrix given a sampling of its entries. Such problems are of considerable interest in a diverse set of fields including control, system identification, statistics and signal processing. Although the general low rank matrix completion problem is …
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A Supervised Low-Rank Matrix Decomposition for Matching
… rely on techniques such as sparse coding and low-rank matrix decomposition. Those build a generative representation of the data that on the one hand, attempts capturing all the information descriptive of an identity; on the other hand, training and testing are complex to allow those algorithms …
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Novel Fast Algorithms For Low Rank Matrix Approximation
<p>Recent advances in matrix approximation have seen an emphasis on randomization techniques in which the goal was to create a sketch of an input matrix. This sketch, a random submatrix of an input matrix, having much fewer rows or columns, still preserves its relevant features. In one of such …
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Faster streaming algorithms for low-rank matrix approximations
Low-rank matrix approximations are used in a significant number of applications. We present new algorithms for generating such approximations in a streaming fashion that expand upon recently discovered matrix sketching techniques. We test our approaches on real and synthetic data to explore runtime …
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Complex data analytics via sparse, low-rank matrix approximation
… Specifically, we have achieved the following: 1) we develop Exemplar-based low-rank sparse Matrix Decomposition (EMD), a novel method for fast clustering large-scale data by incorporating low-rank approximations into matrix decomposition-based clustering; 2) we propose ECKF, a general …
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Over-parameterized low-rank matrix estimation: Theory, algorithms, applications
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Applications of low-rank matrix recovery methods in computer vision
… In this thesis, we harness recent advances in low-rank matrix recovery via convex optimization techniques to solve real problems in computer vision. This thesis also provides some theoretical analysis that extends existing results to new observation models. Low-rank matrix approximations are a …
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Nuclear norm penalized LAD estimator for low rank matrix recovery
In the thesis we propose a novel method for low rank matrix recovery. We study the framework using absolute deviation loss function and nuclear penalty. While nuclear norm penalty is widely utilized heuristic method for shrinkage to low rank solution, the absolute deviation loss function is rarely …
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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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Advances in Sparse and Low Rank Matrix Optimization for Machine Learning Applications
… exhibit natural formulations as cardinality or rank constrained optimization problems. Sparse solutions are desirable for their interpretability and storage benefits. Moreover, in the machine learning setting, sparse solutions exhibit superior model generalization and have a natural …
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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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Inertial iterative thresholding with applications to sparse and low-rank signal recovery
… than the dimension of the signal. Another is low-rank matrix completion where one wants to recover a low-rank matrix from a subset of revealed entries. A third example is robust principle component analysis (RPCA) where one is given a data matrix and would like to decompose it into a low-rank …
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Exploring structural regularities for robust 3D reconstruction of urban scenes
… regularities in visual data which give rise to a low-rank matrix structure, and develop a series of tools to recover them from images and videos. After reviewing the recent developments of convex optimization techniques for low-rank matrix recovery, we propose a novel 3D reconstruction approach …
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Sampling-based algorithms for dimension reduction
Can one compute a low-dimensional representation of any given data by looking only at its small sample, chosen cleverly on the fly? Motivated by the above question, we consider the problem of low-rank matrix approximation: given a matrix A..., one wants to compute a rank-k matrix (where k << min{m, …
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Algorithms and Theory for Robust PCA and Phase Retrieval
… (PCA), which aims to recover an unknown low-rank matrix from a corrupted and partially-observed matrix. </p> <p>The robust PCA problem, originally nonconvex itself, has been solved via a convex relaxation based approach \emph{principal component pursuit} (PCP) in the literature. </p> …
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Efficient and guaranteed algorithms for sparse inverse problems
… vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of the jointly sparse signals, has linearly independent nonzero rows. In this case, the Multiple Signal Classification (MUSIC) algorithm, originally proposed by Schmidt for the …
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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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Algorithms for matrix completion
We consider collaborative filtering methods for matrix completion. A typical approach is to find a low rank matrix that matches the observed ratings. However, the corresponding problem has local optima. In this thesis, we study two approaches to remedy this issue: reference vector method and trace …
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Novel Monte Carlo Methods for Large-Scale Linear Algebra Operations
… solving large-scale linear systems, constructing low-rank matrix approximation, and approximating the extreme eigenvalues/ eigenvectors, across modern distributed and parallel computing architectures. First of all, we revisit the classical Ulam-von Neumann Monte Carlo algorithm and derive the …
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Numerical simulation of microwave heating of a target with temperature dependent electrical properties in a single-mode cavity
… fields inside a high-Q cavity in the presence of low-loss target. In our problem, the dependence of the electrical conductivity on temperature increases the complexity of the problem. Because the electrical conductivity depends on temperature, the electromagnetic fields must be recomputed as the …
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