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 “"Sparse Linear Regression"”.
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A General Framework of Large-Scale Convex Optimization Using Jensen Surrogates and Acceleration Techniques
… algorithms for several applications including Sparse Linear Regression (Image Deblurring), Positron Emission Tomography, X-Ray Transmission Tomography, Logistic Regression, Sparse Logistic Regression and Automatic Relevance Determination for X-Ray Transmission Tomography.</p>
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High-Dimensional Inference with Heterogeneous Data
… in high-dimensional, heterogeneous generalized linear models (GLMs). In a canonical model of heterogeneous regression known as 'Mixed Sparse Linear Regression', we bring novel evidence towards the existence of a statistical price to pay for computational efficiency. In the high-dimensional …
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Distributed Supervised Statistical Learning
… supervised statistical learning where sparse linear regression analysis is performed in a distributed framework. These methods are frequently applied in a variety of disciplines tackling large scale datasets analysis, including engineering, economics, and finance. In distributed …
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On the equivalence of sparse statistical problems
… possible in the high-dimensional setting. Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR) are two problems that have a wide range of applications and have attracted a tremendous amount of attention in the last two decades as canonical examples of statistical …
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Tensors, sparse problems and conditional hardness
… we make a connection between two ubiquitous sparse problems: Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR). We show how to efficiently transform a blackbox solver for SLR into an algorithm for SPCA. Assuming the SLR solver satisfies prediction error guarantees …
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Spectral methods and computational trade-offs in high-dimensional statistical inference
… a semi-definite programming algorithm for the sparse principal component analysis (PCA) problem, and analyse its theoretical performance using the perturbation bounds we derived earlier. It turns out that the parameter regime in which our estimator is consistent is strictly smaller than the …
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Analytics under Variability, Volume, and Velocity with Applications to Sustainability and Healthcare
… our methodology enables the estimation of sparse linear regression models where the underlying regression coefficients are allowed to vary slowly and sparsely under some graph-based temporal or spatial structure. In Chapter 3, we take a step toward the stabilization of decision tree models …
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Reducibility and Statistical-Computational Gaps from Secret Leakage
… mapping among problems representable as a sparse submatrix signal plus a noise matrix, which is similar to the common starting hardness assumption of planted clique (PC). The insight in this work is that a slight generalization of the planted clique conjecture – secret leakage planted …
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Dimensionality reduction in immunology : from viruses to cells
… We propose a computational tool rooted in nonlinear dimensionality reduction which overcomes these limitations, and automatically identifies phenotypes based on a two-dimensional distillation of the cellular data; the latter feature facilitates unbiased visualization of high dimensional …