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 142 for “"Dimension reduction."”.
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Robust and Constrained Dimension Reduction
"The well-known ""curse of dimensionality"" makes high-dimensional data analysis unusually challenging. Dimension reduction plays a valuable role in enabling certain statistical analyses performed in a parsimonious way. The canonical correlation (CANCOR) method developed by Fung et al. (2002) is …
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Sufficient Dimension Reduction with Missing Data
Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) …
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Dimension Reduction on Measures of Impulsivity
… and the second being a comparison of dimension reduction techniques for quantitative data. Results from a Principal Components Analysis (PCA) were compared with results from a novel dimension technique known as Local Linear Embedding (LLE). LLE is an analysis of dimension reduction for …
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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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Building a Nonparametric Model After Dimension Reduction
… of covariates is no easy task. We consider using dimension reduction before building a parametric or spline model. The dimension reduction procedure is based on a canonical correlation analysis on the predictor variables and a spline basis generated for the response variable. One important …
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Model-Free Variable Selection through Sufficient Dimension Reduction
… connection between the fields of sufficient dimension reduction and variable selection to develop new theory and methods for model-free variable selection. After developing the natural connection between sufficient dimension reduction and model-free variable selection we introduce two …
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Contributions to modeling parasite dynamics and dimension reduction
… parasite dynamics (Chapter 2) and complementary dimensionality analysis (Chapter 3). In the first project, we study a longitudinal data of infection with the parasite Giardia lamblia among children in Kenya. Understanding the infection and recovery rate from parasitic infections is valuable for …
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Dimension Reduction and Clustering for Interactive Visual Analytics
When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. …
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Multivariate Nonstationary Time Series: Spectrum Analysis and Dimension Reduction
… time series analysis: spectrum analysis and dimension reduction. The first part of the dissertation introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary …
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Semiparametric Approaches for Dimension Reduction Through Gradient Descent on Manifold
High-dimensional data arises at an unprecedented speed across various fields. Statistical models might fail on high-dimensional data due to the "curse of dimensionality". Sufficient dimension reduction (SDR) is to extract the core information through low-dimensional mapping so that efficient …
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Sparse group sufficient dimension reduction and covariance cumulative slicing estimation
… et al., 2010) to the framework of the sufficient dimension reduction. We propose a method called the sparse group sufficient dimension reduction (sgSDR) to conduct group and within group variable selections simultaneously without assuming a specific model structure on the regression function. …
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Topics in dimension reduction and missing data in statistical discrimination.
… first chapter, we define the concept of linear dimension reduction, review some popular linear dimension reduction procedures, discuss background research that we use in chapters two and three, and give a brief outline of the dissertation contents. In chapter two, we derive a linear dimension …
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Linear Dimension Reduction Approximately Preserving Level-Sets of the 1-Norm
… is a set of N</p><p>points in \R^D , the target dimension k may be chosen as C ln^2 (N^{c+2})/(\epsilon^2(1 −\epsilon )^2), with</p><p>C a constant and \epsilon > N^{−c} , to ensure all pairs of points of X of distance at least 8\epsilon^2</p><p>are treated this way, with failure probability at …
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Dimension reduction and efficient recommender system for large-scale complex data
… we address three challenging issues: sufficient dimension reduction for longitudinal data, nonignorable missing data with refreshment samples, and large-scale recommender systems. In the first part of this thesis, we incorporate correlation structure in sufficient dimension reduction for …
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Bridging Cognitive Gaps Between User and Model in Interactive Dimension Reduction
High-dimensional data is prevalent in all domains but is challenging to explore. Analysis and exploration of high-dimensional data are important for people in numerous fields. To help people explore and understand high-dimensional data, Andromeda, an interactive visual analytics tool, has been …
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Nonparametric variable selection and dimension reduction methods and their applications in pharmacogenomics
… response. Because whole-genome data are high dimensional and their relationships to drug response are complicated, we are developing a variety of nonparametric methods, including variable selection using local regression and extended dimension reduction techniques, to detect nonlinear patterns …
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Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction
… we develop techniques to scale BO to high dimension by exploiting a special structure arising when the objective function varies only in a low-dimensional subspace. Such a subspace can be detected using the (randomized) method of Active Subspaces. We propose a multifidelity active subspace …
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Enabling Efficient Uncertainty Quantification of Turbulent Combustion Simulations via Kinetic Dimension Reduction
… simulations. Various surrogate model and dimension reduction techniques have previously been applied in order to reduce the cost of forward uncertainty propagation in combustion simulations, but these are often limited to low-dimensional, simple combustion cases with scalar solution …
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Dimension reduction algorithms for near-optimal low-dimensional embeddings and compressive sensing
… we establish theoretical guarantees for several dimension reduction algorithms developed for applications in compressive sensing and signal processing. In each instance, the input is a point or set of points in d-dimensional Euclidean space, and the goal is to find a linear function from Rd into …
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