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 31 for “"Matrix decomposition"”.
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A Supervised Low-Rank Matrix Decomposition for Matching
… 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 to be …
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Multivariate volatility modelling in modern finance
… gained from the more complicated multivariate matrix decomposition models can be used to better forecast the covariance matrix and produce a Value at Risk estimate which more appropriately describes fat-tailed financial time-series.
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The Structural Representation of Three -Way Proximity Data
… principle. Order-constrained matrix decomposition can be regarded as a combinatorial data analytic meta-technique, providing a unifying framework for evaluating the differential merits of continuous and discrete structural representations of proximity matrices. We introduce a …
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Statistical methods for learning sparse features
… chapter 2, this thesis considers the problem of matrix decomposition that arises in many real applications such as gene repressive identification and context mining. The goal is to retrieve a multi- layer low-rank sparse decomposition from a high dimensional data matrix. Existing algorithms are …
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Fast algorithms for small particle scattering problems
… volume integral equations and produces a dense matrix equation to be solved. Computationally, the method requires the solution of large dense systems of linear equations, and various iterative methods have been employed in the literature for the purpose. In this work, two distinct methods are …
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Complex data analytics via sparse, low-rank matrix approximation
… 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 model for large-scale Evolutionary Clustering based on …
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Design and evaluation of an adaptable vector coprocessor for multicores
… transform, finite impulse response filter, matrix multiplication and LU matrix decomposition. As an additional experiment, a reconfigurable unit was added to the lane and configured as either a combined floating-point multiply/add or a floating-point divide to better match the needs of …
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Numerical and experimental analysis of circularly polarized radiating line antennas
… described. Examples showing vectorization of the matrix fill, the matrix decomposition, and the computation of far fields are given. Performance statistics showing the improvement obtained from vectorization are provided.
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Algorithms and software for efficient tensor decompositions
… computational and statistical challenges. Tensor decomposition techniques address these issues by extracting low-dimensional representations, reducing storage and computational cost, and enabling efficient analysis and imputation. This thesis develops new algorithms and scalable software for …
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Statistical Methods for Extracting and Comparing Patterns in Multimodal Neuroimaging Studies
… we propose a new method involving penalized matrix decomposition, which identifies spatial patterns in the brain that are highly predictive of disease status but are uncorrelated with age. This is a step towards improving the utility of observational data in developing more interpretable …
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Enhancing Generalization in Sketch-Based Image Retrieval through Single and Multi-Source Domain Adaptation
… addressed through the use of low-rank matrix decomposition and online dictionary learning techniques. Overall, this work provides a comprehensive approach to enhanc- ing SBIR system performance by directly tackling generalization limitations through principled empirical assessment and …
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A Unified Robust Minimax Framework for Regularized Learning Problems
… they are related to the loss function and data matrix in a given statistic model. In this work, we propose a robust minimax formulation to interpret the relationship between data and regularization terms for a large class of loss functions. We show that various regularization terms are …
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Thermo-Mechanical Behavior of Polymer Composites Exposed to Fire
One of the most critical issues for Polymer Matrix Composites (PMCs) in naval applications is the structural performance of composites at high temperature such as that experienced in a fire. A three-dimensional model including the effect of orthotropic viscoelasticity and decomposition is developed …
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A Design Space Exploration Process for Large Scale, Multi-Objective Computer Simulations
… method combines the graph and sparse matrix decomposition methods used by the electrical engineering community with the results of a screening test to create a quantitative method for partitioning large scale, black-box systems. An ANOVA analysis of the results of a screening test can …
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Data analysis in proteomics novel computational strategies for modeling and interpreting complex mass spectrometry data
… in a full-scale proteomic analysis. Secondly, matrix decomposition and projection approaches are explored; these use an eigen-decomposition to extract general trends from groups of related spectra. A data visualization approach is demonstrated using these techniques, capable of visualizing …
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Model selection in compositional spaces
… a model. In this thesis, we present a space of matrix decomposition models defined by the composition of a small number of motifs of probabilistic modeling, including clustering, low rank factorizations, and binary latent factor models. This compositional structure can be represented by a …
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Multidisciplinary design of thermally radiating structures using a level set based topology optimization approach
… our topology optimization approach. The CUR matrix decomposition is best suited for identifying the principal designs and also aids in categorizing the solution topologies. These contributions are demonstrated on several 2D example problems where the goal is to identify thermally efficient …
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Semidenite representations with applications in estimation and inference
… this realization problem as a structured matrix decomposition problem and show how it can be approached using a semidenite optimization formulation. We establish sucient conditions on the parameters and structure of an underlying Gaussian tree model so that our methods can recover it from …
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Development of phenomenologically-based distribution fitting procedures and spatial processes for mixed population soil properties
… a robust trend surface procedure and a new matrix decomposition procedure were developed. These ancillary procedures were shown to be useful in other engineering applications.
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Different Multiple Input Multiple Output Systems
… proposed for VBLAST. The SVD (Singular Value Decomposition) based system is envisioned as a highly effective MIMO technique in a TDD (Time Division Duplex) framework. Such a system operates by adapting the constellation size across different subchannels. In this work we study the VBLAST and …
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