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 10 of 10 for “"Sparse principal component analysis"”.
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New optimization approaches to matrix factorization problems with connections to natural language processing
… 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 robust optimization, and demonstrate …
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Statistical Learning with Discrete Structures: Statistical and Computational Perspectives
… problems in high-dimensional statistics: sparse Principal Component Analysis (PCA) and Gaussian Graphical Models. These are notoriously hard optimization problems---we explore computationally friendlier estimators based on Mixed Integer Programming (MIP) under suitable statistical …
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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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Cardinality Constrained Optimization Problems
In this thesis, we examine optimization problems with a constraint that allows for only a certain number of variables to be nonzero. This constraint, which is called a cardinality constraint, has received considerable attention in a number of areas such as machine learning, statistics, …
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Unsupervised Feature Extraction Techniques for Plasma Semiconductor Etch Processes
… algorithms have been proposed for OES data analysis and the algorithm properties have been explored with the aid of both arti¯cial and industrial benchmark data sets. The ¯rst algorithm, AWSPCA (AdaptiveWeighting Sparse Principal Component Analysis), is developed for dimension reduction with …
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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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Statistical Inference and the Sum of Squares Method
… graphs. Additionally, we prove a lower bound for sparse principal component analysis (PCA), showing that subexponential-size Sum of Squares semidefinite programs are needed to improve on the provable guarantees of existing spectral algorithms for sparse PCA. Our approach to algorithms and lower …
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Statistical methods for learning sparse features
… it is appealing if we can extract the hidden sparse structure of the data since sparse structures allow us to understand and interpret the information better. The aim of this thesis is to develop algorithms that can extract such hidden sparse structures of the data in the context of both …