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 9 of 9 for “"Low-Rank Matrix Recovery"”.
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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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Exploring structural regularities for robust 3D reconstruction of urban scenes
… look into the problem of structure and motion recovery directly from one or more large planes in the scene. We develop a new SFM method that generates high-quality reconstruction results in a short time, while avoiding several practical difficulties of conventional methods. Then, we show how …
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Essays in Problems in Sequential Decisions and Large-Scale Randomized Algorithms
… We propose a two-step sensing scheme for the low-rank matrix recovery problem which requires far less storage space and has much lower computational complexity than other state-of-art methods based on nuclear norm minimization. We introduce a fast iterative reweighted least squares algorithm, …
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Optimism and Robustness: Learning From Structured and Semi-Random Inputs
… inputs (semi-random inputs). In the context of low-rank matrix recovery problems, this means a monotone adversary can add arbitrary data from the distribution to break the necessary regularity conditions satisfied by fully random inputs. In Chapter 4, we study the matrix completion problem, …
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ON RICCATI EQUATIONS IN NONCONVEX OPTIMIZATION
… guaranteed. These cases include problems such as low-rank matrix recovery, dictionary learning, and certain formulations of optimal control, which have optimization landscapes that are well-behaved in the sense where every local minimum is global and critical points are connected through …
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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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Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery
… on the information-theoretic limits of unique low-rank matrix recovery, we finally bridge this gap, and derive an optimal sample complexity result for BD with generic bases or frames. We show that for BD of an arbitrary pair (resp. all pairs) of vectors in $\bbC^n$, with sparsity constraints of …
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Computable Performance Analysis of Recovering Signals with Low-dimensional Structures
… reconstruction of signals by exploiting their low-dimensional structures, particularly, the sparsity, the block-sparsity, the low-rankness, and the low-dimensional manifold structures of general nonlinear data sets. The reconstruction performance of these signals relies heavily on the structure …