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 11 of 11 for “"L1 regularization"”.
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COMPRESSIVE SENSING BASED IMAGE RECONSTRUCTION FOR COMPUTED TOMOGRAPHY DOSE REDUCTION
… integral model (AIM). Recently, the Lp (0<p<1) regularization has attracted a great attention because it can generate sparser solutions than the L1 regularization. We derive several analytic thresholding representations for Lp (0≤p≤1) regularization and develop a corresponding general …
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High dimensional feature selection under interactive models
… feature selection procedure, called sequential L1 regularization algorithm (SLR), under high dimensional space by considering both the main effect features and the interactive effect features in the context of generalized linear models. The theoretical property of SLR is explored and the …
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Non-invasive IC tomography using spatial correlations
… The sparse representation suggests using the L1-regularization (the compressive sensing theory). We show how to use the compressive sensing theory to improve post-silicon characterization. We also address the problem by adding spatial constraints directly to the traditional L2-minimization. …
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Fast superresolution based on a network structure trained using sparse coding
… that includes the square of the L2 norm and a regularization term containing the L1 norm of the sparse vector. This sets up a regularized least squares solution. The L1 norm is preferred because it promotes the sparseness of the solution. The L0 norm term in the regularization parameter may …
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Metabolic Pathway Optimization with Data Driven Approaches
… the probability of dominance of each step, while L1 regularization is applied to select system parameters that contribute most to the prediction. For better predictions, a neural network is used for the prediction of distribution of control coefficients based on the FCC summation property. The …
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Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights
… popular sparse learning methods (e.g., based on L1 regularization). Our open-source implementation (L0Learn) can handle instances with millions of features and run up to 3x faster than state-of-the-art sparse learning toolkits. In the second chapter, we propose an exact, scalable approach for …
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Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data
… structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the …
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Source localization via time difference of arrival
… is proposed. This is formulated as an f1-regularization problem, where the f1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different computational costs. The first method takes a semi-definite relaxation (SDR) …
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New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation
… an algorithm to solve the optimization with a L1 regularization term, namely the Lasso-type problem. The algorithm developed in this chapter can greatly reduce the computational complexity in Chapter 1, Chapter 2 and Chapter 3, where we try to realize sparse identification. The challenge to …
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Knowledge-fused Identification of Condition-specific Rewiring of Dependencies in Biological Networks
Gene network modeling is one of the major goals of systems biology research. Gene network modeling targets the middle layer of active biological systems that orchestrate the activities of genes and proteins. Gene network modeling can provide critical information to bridge the gap between causes and …