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Low Rank and Sparse Modeling for Visual Analysis

, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both;

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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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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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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications;

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Latent Variable Analysis and Signal Separation

fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.;

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Turbo Message Passing Algorithms for Structured Signal Recovery

authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed;

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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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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Sparse Modeling

formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you;

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Sparse Modeling

, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The;

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Low overhead Communications in IoT Networks

size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead;

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Low overhead Communications in IoT Networks

size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead;

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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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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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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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Simulation with Visual SLAM and AweSim

four parts: Introduction to Simulation, Visual SLAM Network Modeling and AweSim, Simulation Analysis, and Visual SLAM Discrete, Continuous and;

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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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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Low-rank Coals for Power Generation, Fuel and Chemical Production

Low-Rank Coals for Power Generation, Fuel and Chemical Production provides a thorough introduction to lignite (brown coal) 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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Multi-Asset Risk Modeling

Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed;

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Sparse Coding and Its Applications in Computer Vision

directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and;

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Low Rank Approximation

extensively rewritten revision. It contains new chapters and sections that introduce the topics of: * variable projection for structured low-rank;

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Image Understanding Using Sparse Representations

theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms;

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Damage Modeling of Composite Structures

delamination analysis of composites under shear and for laminates under low-velocity impact are all covered at length. A concluding chapter;

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Sparse Grids and Applications Miami 2016

Sparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes;

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