Low Rank Models In Visual Analysis

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Low-Rank Models in Visual Analysis

Author : Zhouchen Lin
Publisher : Academic Press
ISBN 13 : 0128127325
Total Pages : 260 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Low-Rank Models in Visual Analysis by : Zhouchen Lin

Download or read book Low-Rank Models in Visual Analysis written by Zhouchen Lin and published by Academic Press. This book was released on 2017-06-06 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications Provides a full and clear explanation of the theory behind the models Includes detailed proofs in the appendices

Low-Rank and Sparse Modeling for Visual Analysis

Author : Yun Fu
Publisher : Springer
ISBN 13 : 331912000X
Total Pages : 240 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Low-Rank and Sparse Modeling for Visual Analysis by : Yun Fu

Download or read book Low-Rank and Sparse Modeling for Visual Analysis written by Yun Fu and published by Springer. This book was released on 2014-10-30 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Deep Learning through Sparse and Low-Rank Modeling

Author : Zhangyang Wang
Publisher : Academic Press
ISBN 13 : 012813660X
Total Pages : 296 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang

Download or read book Deep Learning through Sparse and Low-Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-11 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Low-Rank Approximation

Author : Ivan Markovsky
Publisher : Springer
ISBN 13 : 3319896202
Total Pages : 272 pages
Book Rating : 4.3/5 (198 download)

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Book Synopsis Low-Rank Approximation by : Ivan Markovsky

Download or read book Low-Rank Approximation written by Ivan Markovsky and published by Springer. This book was released on 2018-08-03 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required. The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of: • variable projection for structured low-rank approximation;• missing data estimation;• data-driven filtering and control;• stochastic model representation and identification;• identification of polynomial time-invariant systems; and• blind identification with deterministic input model. The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB^® /Octave examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis. “Each chapter is completed with a new section of exercises to which complete solutions are provided.” Low-Rank Approximation (second edition) is a broad survey of the Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.

Computer Vision for Microscopy Image Analysis

Author : Mei Chen
Publisher : Academic Press
ISBN 13 : 0128149736
Total Pages : 230 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computer Vision for Microscopy Image Analysis by : Mei Chen

Download or read book Computer Vision for Microscopy Image Analysis written by Mei Chen and published by Academic Press. This book was released on 2020-12-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

Author : Marcelo Bertalmío
Publisher : Academic Press
ISBN 13 : 0128138955
Total Pages : 324 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Vision Models for High Dynamic Range and Wide Colour Gamut Imaging by : Marcelo Bertalmío

Download or read book Vision Models for High Dynamic Range and Wide Colour Gamut Imaging written by Marcelo Bertalmío and published by Academic Press. This book was released on 2019-11-06 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies. Presents the underlying vision science principles and models that are essential to the emerging technologies of HDR and WCG Explores state-of-the-art techniques for tone and gamut mapping Discusses open challenges and future directions of HDR and WCG research

Spectral Geometry of Shapes

Author : Jing Hua
Publisher : Academic Press
ISBN 13 : 0128138424
Total Pages : 152 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Spectral Geometry of Shapes by : Jing Hua

Download or read book Spectral Geometry of Shapes written by Jing Hua and published by Academic Press. This book was released on 2020-01-15 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource. Presents the latest advances in spectral geometric processing for 3D shape analysis applications, such as shape classification, shape matching, medical imaging, etc. Provides intuitive links between fundamental geometric theories and real-world applications, thus bridging the gap between theory and practice Describes new theoretical breakthroughs in applying spectral methods for non-isometric motion analysis Gives insights for developing spectral geometry-based approaches for 3D shape analysis and deep learning of shape geometry

Probabilistic Graphical Models for Computer Vision

Author : Qiang Ji
Publisher : Academic Press
ISBN 13 : 012803467X
Total Pages : 294 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Probabilistic Graphical Models for Computer Vision by : Qiang Ji

Download or read book Probabilistic Graphical Models for Computer Vision written by Qiang Ji and published by Academic Press. This book was released on 2019-11 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction

Intelligence Science and Big Data Engineering. Visual Data Engineering

Author : Zhen Cui
Publisher : Springer Nature
ISBN 13 : 3030361896
Total Pages : 594 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Intelligence Science and Big Data Engineering. Visual Data Engineering by : Zhen Cui

Download or read book Intelligence Science and Big Data Engineering. Visual Data Engineering written by Zhen Cui and published by Springer Nature. This book was released on 2019-11-28 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

Cardiovascular and Coronary Artery Imaging

Author : Ayman S. El-Baz
Publisher : Academic Press
ISBN 13 : 0128227079
Total Pages : 360 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Cardiovascular and Coronary Artery Imaging by : Ayman S. El-Baz

Download or read book Cardiovascular and Coronary Artery Imaging written by Ayman S. El-Baz and published by Academic Press. This book was released on 2021-11-24 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging. Takes an integrated approach to cardiovascular and coronary imaging, covering machine learning, deep learning and reinforcement learning approaches Covers state-of-the-art approaches for automated non-invasive systems for early cardiovascular disease diagnosis Provides a perspective on future cardiovascular imaging and highlights areas that still need improvement

Generalized Low Rank Models

Author : Madeleine Udell
Publisher :
ISBN 13 : 9781680831405
Total Pages : 142 pages
Book Rating : 4.8/5 (314 download)

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Book Synopsis Generalized Low Rank Models by : Madeleine Udell

Download or read book Generalized Low Rank Models written by Madeleine Udell and published by . This book was released on 2016-05-03 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. We propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.

Group and Crowd Behavior for Computer Vision

Author : Vittorio Murino
Publisher : Academic Press
ISBN 13 : 0128092807
Total Pages : 438 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Group and Crowd Behavior for Computer Vision by : Vittorio Murino

Download or read book Group and Crowd Behavior for Computer Vision written by Vittorio Murino and published by Academic Press. This book was released on 2017-04-18 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. Presents the first book to cover the topic of modeling and analysis of groups in computer vision Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms Focuses on group and crowd analysis metrics Discusses real industrial systems dealing with the problem of analyzing groups and crowds

Skeletonization

Author : Punam K Saha
Publisher : Academic Press
ISBN 13 : 0081012926
Total Pages : 412 pages
Book Rating : 4.0/5 (81 download)

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Book Synopsis Skeletonization by : Punam K Saha

Download or read book Skeletonization written by Punam K Saha and published by Academic Press. This book was released on 2017-06-06 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Skeletonization: Theory, Methods and Applications is a comprehensive reference on skeletonization, written by the world’s leading researchers in the field. The book presents theory, methods, algorithms and their evaluation, together with applications. Skeletonization is used in many image processing and computer vision applications such as shape recognition and analysis, shape decomposition and character recognition, as well as medical imaging for pulmonary, cardiac, mammographic applications. Part I includes theories and methods unique to skeletonization. Part II includes novel applications including skeleton-based characterization of human trabecular bone micro-architecture, image registration and correspondence establishment in anatomical structures, skeleton-based fast, fully automated generation of vessel tree structure for clinical evaluation of blood vessel systems. Offers a complete picture of skeletonization and its application to image processing, computer vision, pattern recognition and biomedical engineering Provides an in-depth presentation on various topics of skeletonization, including principles, theory, methods, algorithms, evaluation and real-life applications Discusses distance-analysis, geometry, topology, scale and symmetry-analysis in the context of object understanding and analysis using medial axis and skeletonization

Computer Vision for Assistive Healthcare

Author : Leo Marco
Publisher : Academic Press
ISBN 13 : 0128134461
Total Pages : 396 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computer Vision for Assistive Healthcare by : Leo Marco

Download or read book Computer Vision for Assistive Healthcare written by Leo Marco and published by Academic Press. This book was released on 2018-05-15 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition. In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring. Gives the state-of-the-art computer vision techniques and tools for assistive healthcare Includes a broad range of topic areas, ranging from image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics Presents a wide range of application areas, ranging from mobility, sensory substitution, and safety and security, to mental and physical rehabilitation and training Written by leading researchers in this growing field of research Describes the outstanding research challenges that still need to be tackled, giving researchers good indicators of research opportunities

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Author : Thierry Bouwmans
Publisher : CRC Press
ISBN 13 : 1315353539
Total Pages : 510 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Handbook of Robust Low-Rank and Sparse Matrix Decomposition by : Thierry Bouwmans

Download or read book Handbook of Robust Low-Rank and Sparse Matrix Decomposition written by Thierry Bouwmans and published by CRC Press. This book was released on 2016-09-20 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Neural Information Processing

Author : Derong Liu
Publisher : Springer
ISBN 13 : 3319701363
Total Pages : 912 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Neural Information Processing by : Derong Liu

Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-11-07 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.

Deep Learning through Sparse and Low-Rank Modeling

Author : Zhangyang Wang
Publisher : Academic Press
ISBN 13 : 0128136596
Total Pages : 296 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang

Download or read book Deep Learning through Sparse and Low-Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-26 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications