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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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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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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while;

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Robust Nonparametric Statistical Methods

Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers;

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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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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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

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Turbo Message Passing Algorithms for Structured Signal Recovery

signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to;

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

contemporary developments related to the analysis and learning of images, shapes and forms. It covers mathematical models as well as fast computational;

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Low Rank Approximation

This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local;

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Statistical Factor Analysis And Related Methods

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory;

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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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Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional;

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Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers;

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Business & Financial Models

scenarios in a safe, low-risk environment, to consider what it is aiming to achieve, and to prioritize accordingly. Business and Financial;

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Low-carbon Economics

By providing a theoretical analysis for the issues of climate change, carbon emission mitigation, energy-saving and low-carbon development;

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Big Data in Omics and Imaging, Two Volume Set

heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank;

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Linear Models And Regression With R

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same;

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Learning Approaches in Signal Processing

forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman;

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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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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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Visual Analysis of Behaviour

This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives;

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Probabilistic Ranking Techniques in Relational Databases

Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed;

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Big Data in Omics and Imaging: Association Analysis

algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern;

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Big Data in Omics and Imaging: Association Analysis

, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association;

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Linear Algebra and Linear Models

as rank additivity, inequalities for eigenvalues and singular values; a new chapter on linear mixed models; over seventy additional;

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