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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace;

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Fundamentals of Matrix Computations

decomposition based on adaptive over-complete dictionary, lower bounds for the low-rank matrix approximation and a semi-smoothing augmented lagrange;

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Turbo Message Passing Algorithms for Structured Signal Recovery

the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal;

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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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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem;

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Robust Representation for Data Analytics

the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including;

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Fundamentals of Matrix Computations

componentwise error analysis, reorthogonalization, and rank-one updates of the QR decomposition, Fundamentals of Matrix Computations, Second Edition;

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Matrix Computations

on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating;

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Computation of Generalized Matrix Inverses and Applications

the calculation of {i,j,...,k} inverse and the Moore-Penrose inverse. Then, the results of LDL* decomposition of the full rank polynomial;

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Computation of Generalized Matrix Inverses and Applications

calculation of {i,j,...,k} inverse and the Moore-Penrose inverse. Then, the results of LDL* decomposition of the full rank polynomial matrix are;

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Matrix Theory

factorization, derives Sylvester's rank formula, introduces full-rank factorization, and describes generalized inverses. After discussions on norms, QR;

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Matrix Theory

factorization, derives Sylvester's rank formula, introduces full-rank factorization, and describes generalized inverses. After discussions on norms, QR;

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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d'Été de Probabilités de Saint-Flour XXXVIII-2008

recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research;

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Introduction to Matrix Theory

decomposition, singular value decomposition, and polar decomposition. Along with Gauss-Jordan elimination for linear systems, it also discusses best;

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Feature Learning and Understanding: Algorithms and Applications

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Feature Learning and Understanding

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Linear Algebra and Matrix Analysis for Statistics

Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the;

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Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 1

, Geometric models for perception-based image processing, Decomposition schemes for nonconvex composite minimization: theory and applications, Low;

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Introduction to High-Dimensional Statistics

sensing, estimation with convex constraints, the slope estimator, simultaneously low rank and row sparse linear regression, or aggregation of a;

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Matrix and Tensor Decompositions in Signal Processing

The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as;

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Low overhead Communications in IoT Networks

. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This;

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Low overhead Communications in IoT Networks

. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This;

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Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their;

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Matrix Based Introduction to Multivariate Data Analysis

procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to;

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Matrix-Based Introduction to Multivariate Data Analysis

procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to;

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Matrix Preconditioning Techniques and Applications

conjugate gradient, multi-level and fast multi-pole methods, matrix and operator splitting, fast Fourier and wavelet transforms, incomplete LU and;

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Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation;

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