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Sparse Approximation with Bases

This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous;

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Low Rank and Sparse Modeling for Visual Analysis

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding;

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Recent Progress in Multivariate Approximation

, approximation by solutions of partial differential equations, application of Riesz bases and frames, multiwavelets and subdivision. The book is an;

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Introduction To Sparse Stochastic Processes

and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.;

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Curves and Surfaces

, held in Paris, France, in June 2014. The conference had the overall theme: Representation and Approximation of Curves and Surfaces and;

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Numerical Fourier Analysis

approximation using Fourier methods. It starts with a detailed discussion of classical Fourier theory to enable readers to grasp the construction;

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Cambridge Studies in Advanced Mathematics

connection with wavelet bases. Sparse matrix representations of these operators can be given in terms of wavelet bases which have important;

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Wavelets

connection with wavelet bases. Sparse matrix representations of these operators can be given in terms of wavelet bases which have important;

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Compressed Sensing and its Applications

matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied;

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Multivariate Wavelet Frames

bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a;

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Sparse representation of High Dimensional Data for Classification

result with regular PCA. The experiments were performed on hyperspectral data and various datasets obtained from University of California, Irvine;

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Sparse Grids and Applications Miami 2016

importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with;

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Sparse Modeling

Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...

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Sparse Modeling

Sparse models are particularly useful in scientific applications, such as biomarker discovery in gen;...

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Rational Bases and Generalized Barycentrics

This three-part volume explores theory for construction of rational interpolation functions for continuous patchwork approximation;

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Image Understanding Using Sparse Representations

paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications;

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Approximation Theory and Algorithms for Data Analysis

of approximation with recent advances in mathematical signal processing, and adopting a constructive approach, in which the development of numerical;

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A Wavelet Tour of Signal Processing

representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept;

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Sparse Coding and Its Applications in Computer Vision

introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value;

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Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance

This book deals with the numerical analysis and efficient numerical treatment of high-dimensional integrals using sparse grids and other;

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Fundamentals of Matrix Computations

Fundamentals of Matrix Computations deals with the concept of matrix computations, a technique of singular value homogenization and its;

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Sparse Representations for Radar with MATLAB (R) Examples

area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB (R) examples to;

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Metric Diophantine Approximation on Manifolds

This 1999 book is concerned with Diophantine approximation on smooth manifolds embedded in Euclidean space, and its aim is to develop a;

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Deep Learning through Sparse and Low-Rank Modeling

-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows;

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Journal of Approximation Theory and Applied Mathematics - 2013 Vol. 1 and Vol. 2

: Approximation theory (with a focus on wavelets) and applications in mathematics like numerical analysis, statistics or financial mathematics. Contents;

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Multivariate Approximation

This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to;

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Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

This book describes, in a basic way, the most useful and effective iterative solvers and appropriate;...

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