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 21 for “"sparse recovery"”.
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Sparse recovery and Fourier sampling
… decade a broad literature has arisen studying sparse recovery, the estimation of sparse vectors from low dimensional linear projections. Sparse recovery has a wide variety of applications such as streaming algorithms, image acquisition, and disease testing. A particularly important subclass of …
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SPARSE RECOVERY BY NONCONVEX LIPSHITZIAN MAPPINGS
… processing fields. The general framework of sparse representation is now a mature concept with solid basis in relevant mathematical fields, such as probability, geometry of Banach spaces, harmonic analysis, theory of computability, and information-based complexity. Together with theoretical …
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Algorithms and lower bounds for sparse recovery
We consider the following k-sparse recovery problem: design a distribution of m x n matrix A, such that for any signal x, given Ax with high probability we can efficiently recover x satisfying IIx - x l, </-Cmink-sparse x' IIx - x'II. It is known that there exist such distributions with m = O(k …
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Mathematical analysis of a dynamical system for sparse recovery
… analysis of a continuous-times system for sparse signal recovery. Sparse recovery arises in Compressed Sensing (CS), where signals of large dimension must be recovered from a small number of linear measurements, and can be accomplished by solving a complex optimization program. While many …
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Discrete and Continuous Sparse Recovery Methods and Their Applications
… space can be exploited to aid in the recovery of the signal of interest. Among all the different forms of low di- mensionality, in this dissertation we focus on the synthesis and analysis models of sparse recovery. This dissertation comprises two major topics. For the first topic, we …
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Algorithms and lower bounds in the streaming and sparse recovery models
… for each possible item we might see. In the sparse recovery model (also known as compressed sensing), input data is a large but sparse vector x [epsilon] Rn, and our goal is to design an m x n matrix [Phi]D, where m << n, such that for any sufficiently sparse x we can efficiently recover a …
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Advances in sparse signal recovery methods
… algorithms, etc. In this thesis we focus on sparse recovery, where the goal is to recover sparse vectors exactly, and to approximately recover nearly-sparse vectors. More precisely, from the short representation of a vector x, we want to recover a vector x* such that the approximation error …
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Dimensionality reduction for sparse and structured matrices
… important case of dimensionality reduction for sparse and structured data. In contrast to popular structure-agnostic methods like Johnson-Lindenstrauss projection and PCA, we seek data compression techniques that take advantage of structure to generate smaller or more powerful compressions. …
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A spin on compressive sensing imaging : reticle-based single-pixel imaging system
… is a signal acquisition technique to recover a sparse vector from only a few linear measurements. CS assumes a sparse vector being sampled and the use of sparsifying dictionaries is required for sampling non-sparse vectors such as images. Optimizing the sensing matrix to improve the recovery …
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Model-based compressive sensing with Earth Mover's Distance constraints
… and develop a variant of IHT, an existing sparse recovery algorithm, that works with approximate model projection algorithms.
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Efficient and guaranteed algorithms for sparse inverse problems
… and reduced-cost acquisition, by exploiting a sparse signal model. Most notably, recovery of the signal by computationally efficient algorithms is guaranteed for certain randomized acquisition systems. However, there is a discrepancy between the theoretical guarantees and practical …
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Non-asymptotic bounds for prediction problems and density estimation.
… The second part of this thesis is related to sparse recovery in the framework of dictionary learning. Let (X,Y) be a random couple with unknown distribution P. Given a collection of functions H, the goal of dictionary learning is to construct a prediction rule for Y given by a linear …
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Recovery of sparse signals and parameter perturbations from parameterized signal models
… proven to be a powerful constraint for signal recovery when the unknown signal has few nonzero elements. If a signal is sparse in a parameterized measurement model, the model parameters must be known to recover the signal. An example of this problem is the recovery of a signal that is a sum of …
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Adaptive OFDM Radar for Target Detection and Tracking
… the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in …
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Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems
… helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received bythe radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimatingthe number, the …
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Sparse Value Function Approximation for Reinforcement Learning
… learning the value function approximation; such sparse methods tend to select relevant features and ignore irrelevant features, thus automating the feature selection process. This dissertation describes three contributions in the area of sparse value function approximation for reinforcement …
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Fast superresolution based on a network structure trained using sparse coding
… to superresolution using a network structure. Sparse representation of image signals forms the cornerstone of our approach and the goal is to obtain resolution enhancement of the low resolution images. I will discuss various dictionary learning methods and also a joint dictionary training …
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Dictionary learning for scalable sparse image representation
… one of the novel processing tools is signal sparse coding which represents signals as linear combinations of a few representational basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and …
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Global optimization methods for localization in compressive sensing
… and optimization. It advocates the sensing of "sparse" signals (i.e., represented using just a few terms from a basis expansion) by using a sampling rate much lower than that required by the Nyquist-Shannon sampling theorem (i.e., twice the highest frequency present in the signal of interest). …
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Applications Of Machine Learning In Biology And Medicine
… for stability.</p> <p>Recent advances in sparse recovery and estimation methods have provided efficient and asymptotically consistent feature selection algorithms.</p> <p>However, these methods lack finite sample error control due to instability.</p> <p>Furthermore, the chances of correct …
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