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 228 for “"low rank"”.
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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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Low rank methods for optimizing clustering
… often have the majority of information in a low rank subspace. By careful exploitation of these low rank structures in clustering problems, we find new optimization approaches that reduce the memory and computational cost.</p> <p>We discuss two cases where this arises. First, we consider the …
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Dynamic speech imaging with low-rank approximation
… However, conventional MRI suffers from low spatiotemporal resolution, which limits its applica-tion in dynamic speech imaging. This thesis presents a novel model-based dynamic MR imaging method to capture speech dynamics in high spatiotemporal resolution. Specifically, high …
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Bounding cohomology for low rank algebraic groups
… We then concentrate on algebraic groups in rank 1 and 2, and prove some new results in their bounding cohomology.
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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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LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS
… High dimensional data usually have intrinsic low-dimensional representations, which are suited for subsequent analysis or processing. Therefore, finding low-dimensional representations is an essential step in many machine learning and data mining tasks. Low-rank and sparse modeling are …
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Studies on Thermal Solution of Low-Rank Fuels
<p>A study was made of solvent extraction of "Leonardite" obtained from the Slack Seam of the Baukol-Noonan coal company, Divide County, Worth Dakota. Apparatus and method are described for the treatment of the Leonardite, the determination of extraction yields, and the separation of products. A …
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Novel Fast Algorithms For Low Rank Matrix Approximation
… matrices which can be orthogonal, random, and allowing fast multiplication by a vector. The Subsampled Randomized Hadamard Transform (SRHT) is the most popular among transforms. An m x n matrix can be multiplied by an n x l SRHT matrix in O(mn log l) arithmetic operations where typically l << …
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Low-rank completion and recovery of correlation matrices
… the problem of matrix completion within a low-rank framework is of particular significance. This dissertation presents the methods of spectral completion and convex relaxation, which have been successfully applied to the particular problem of lowrank completion and recovery of valid …
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The Low-rank Simplicity Bias in Deep Networks
… are inductively biased to find solutions with lower effective rank embeddings. We conjecture that this bias exists because the volume of functions that maps to low effective rank embedding increases with depth. We show empirically that our claim holds true on finite width linear and non-linear …
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Urban scene parsing via low-rank texture patches
… pipeline that detects these salient areas using low-rank texture patches. Areas in images such as building facades contain low-rank textures, which are an intrinsic property of the scene and invariant to viewpoint. The pipeline uses these low-rank patches to automatically rectify images and …
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Low rank decompositions for sum of squares optimization
… coefficient method of SOS, the formulation has a low rank property in its constraints. The low rank property is desirable as it improves computation speed for calculations of barrier gradient and Hessian assembling in many semidefinite programming (SDP) solvers. Currently, SDPT3 solver has a …
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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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Criminal data analysis based on low rank sparse representation
… of a high-dimensional data for finding a low-dimensional space. Many proposed methods fail to overcome the challenges, especially when the data input is high-dimensional, and the clusters have a complex. REGULARLY in high dimensional data, lots of the data dimensions are not related and …
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Low-Rank Distributed Control with Application to Wind Energy
… wake effects in wind farms by developing a low-complexity model of the aerodynamic interaction between wind turbines. The model is used in a series of examples, where the wind turbines coordinate their power productions in order to maximize the power production of the wind farm. The examples …
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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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Low-rank estimation and embedding learning: theory and applications
… the problem of automatically identifying the low-dimensional space that high-dimensional datasets (approximately) lie in based on dimension reduction models: one is low-rank estimation models and the other is embedding learning models. For data matrices, low-rank estimation is to recover an …
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Bounds and Low-Rank Approximation for Controlled Markov Processes
… shed light on fundamental limits, allowing us to distinguish situations where intrinsic noise masks poor decisions from situations where any attempt of improvement is futile. We connect infinite-dimensional linear programming over cones of occupation measures to techniques for …
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Applications of low-rank approximation: complex networks and inverse problems
The use of low-rank approximation is crucial when one is interested in solving problems of large dimension. In this case, the matrix with reduced rank can be obtained starting from the singular value decomposition considering only the largest components. This thesis describes how the use of the …
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