Deep Learning through Sparse and Low Rank Modeling Book

Deep Learning through Sparse and Low Rank Modeling


  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Release Date : 2019-05-15
  • Genre: Computers
  • Pages : 300
  • ISBN 10 : 9780128136591

DOWNLOAD BOOK
Deep Learning through Sparse and Low Rank Modeling 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

Deep Learning through Sparse and Low Rank Modeling Book

Deep Learning through Sparse and Low Rank Modeling


  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Release Date : 2019-04-11
  • Genre: Computers
  • Pages : 296
  • ISBN 10 : 9780128136607

DOWNLOAD BOOK
Deep Learning through Sparse and Low Rank Modeling 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 and Sparse Modeling for Visual Analysis Book

Low Rank and Sparse Modeling for Visual Analysis


  • Author : Yun Fu
  • Publisher : Springer
  • Release Date : 2014-10-30
  • Genre: Computers
  • Pages : 236
  • ISBN 10 : 9783319120003

DOWNLOAD BOOK
Low Rank and Sparse Modeling for Visual Analysis 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.

Inpainting and Denoising Challenges Book

Inpainting and Denoising Challenges


  • Author : Sergio Escalera
  • Publisher : Springer Nature
  • Release Date : 2019-10-16
  • Genre: Computers
  • Pages : 144
  • ISBN 10 : 9783030256142

DOWNLOAD BOOK
Inpainting and Denoising Challenges Excerpt :

The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.

Sparse and Low rank Modeling for Automatic Speech Recognition Book

Sparse and Low rank Modeling for Automatic Speech Recognition


  • Author : Pranay Dighe
  • Publisher : Unknown
  • Release Date : 2019
  • Genre: Uncategoriezed
  • Pages : 133
  • ISBN 10 : OCLC:1091605389

DOWNLOAD BOOK
Sparse and Low rank Modeling for Automatic Speech Recognition Excerpt :

Mots-clés de l'auteur: automatic speech recognition ; deep neural network ; sparsity ; dictionary learning ; low-rank ; principal component analysis ; far-field speech ; information theory.

Handbook of Robust Low Rank and Sparse Matrix Decomposition Book

Handbook of Robust Low Rank and Sparse Matrix Decomposition


  • Author : Thierry Bouwmans
  • Publisher : CRC Press
  • Release Date : 2016-09-20
  • Genre: Computers
  • Pages : 520
  • ISBN 10 : 9781315353531

DOWNLOAD BOOK
Handbook of Robust Low Rank and Sparse Matrix Decomposition 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.

Low Rank Models in Visual Analysis Book

Low Rank Models in Visual Analysis


  • Author : Zhouchen Lin
  • Publisher : Academic Press
  • Release Date : 2017-06-06
  • Genre: Computers
  • Pages : 260
  • ISBN 10 : 9780128127322

DOWNLOAD BOOK
Low Rank Models in Visual Analysis 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

Generalized Low Rank Models Book

Generalized Low Rank Models


  • Author : Madeleine Udell
  • Publisher : Unknown
  • Release Date : 2016-05-03
  • Genre: Uncategoriezed
  • Pages : 142
  • ISBN 10 : 1680831402

DOWNLOAD BOOK
Generalized Low Rank Models 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.

Handbook of Robust Low Rank and Sparse Matrix Decomposition Book

Handbook of Robust Low Rank and Sparse Matrix Decomposition


  • Author : Thierry Bouwmans
  • Publisher : CRC Press
  • Release Date : 2016-09-20
  • Genre: Computers
  • Pages : 520
  • ISBN 10 : 9781498724630

DOWNLOAD BOOK
Handbook of Robust Low Rank and Sparse Matrix Decomposition 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.

Artificial Intelligence  Evolutionary Computing and Metaheuristics Book

Artificial Intelligence Evolutionary Computing and Metaheuristics


  • Author : Xin-She Yang
  • Publisher : Springer
  • Release Date : 2012-07-27
  • Genre: Technology & Engineering
  • Pages : 796
  • ISBN 10 : 9783642296949

DOWNLOAD BOOK
Artificial Intelligence Evolutionary Computing and Metaheuristics Excerpt :

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo search, and multiobjective optimization and many applications. These reviews and chapters not only provide a timely snapshot of the state-of-art developments, but also provide inspiration for young researchers to carry out potentially ground-breaking research in the active, diverse research areas in artificial intelligence, cryptography, machine learning, evolutionary computation, and nature-inspired metaheuristics. This edited book can serve as a timely reference for graduates, researchers and engineers in artificial intelligence, computer sciences, computational intelligence, soft computing, optimization, and applied sciences.

Machine Learning Book
Score: 4
From 1 Ratings

Machine Learning


  • Author : Sergios Theodoridis
  • Publisher : Academic Press
  • Release Date : 2020-02-19
  • Genre: Computers
  • Pages : 1160
  • ISBN 10 : 9780128188040

DOWNLOAD BOOK
Machine Learning Excerpt :

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, dee

Hands On Machine Learning with R Book

Hands On Machine Learning with R


  • Author : Brad Boehmke
  • Publisher : CRC Press
  • Release Date : 2019-11-07
  • Genre: Business & Economics
  • Pages : 456
  • ISBN 10 : 9781000730197

DOWNLOAD BOOK
Hands On Machine Learning with R Excerpt :

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Study on Efficient Sparse and Low rank Optimization and Its Applications Book

Study on Efficient Sparse and Low rank Optimization and Its Applications


  • Author : Jian Lou
  • Publisher : Unknown
  • Release Date : 2018
  • Genre: Algorithms
  • Pages : 238
  • ISBN 10 : OCLC:1066747724

DOWNLOAD BOOK
Study on Efficient Sparse and Low rank Optimization and Its Applications Excerpt :

Sparse and low-rank models have been becoming fundamental machine learning tools and have wide applications in areas including computer vision, data mining, bioinformatics and so on. It is of vital importance, yet of great difficulty, to develop efficient optimization algorithms for solving these models, especially under practical design considerations of computational, communicational and privacy restrictions for ever-growing larger scale problems. This thesis proposes a set of new algorithms to improve the efficiency of the sparse and low-rank models optimization. First, facing a large number of data samples during training of empirical risk minimization (ERM) with structured sparse regularization, the gradient computation part of the optimization can be computationally expensive and becomes the bottleneck. Therefore, I propose two gradient efficient optimization algorithms to reduce the total or per-iteration computational cost of the gradient evaluation step, which are new variants of the widely used generalized conditional gradient (GCG) method and incremental proximal gradient (PG) method, correspondingly. In detail, I propose a novel algorithm under GCG framework that requires optimal count of gradient evaluations as proximal gradient. I also propose a refined variant for a type of gauge regularized problem, where approximation techniques are allowed to further accelerate linear subproblem computation. Moreover, under the incremental proximal gradient framework, I propose to approximate the composite penalty by its proximal average under incremental gradient framework, so that a trade-off is made between precision and efficiency. Theoretical analysis and empirical studies show the efficiency of the proposed methods. Furthermore, the large data dimension (e.g. the large frame size of high-resolution image and video data) can lead to high per-iteration computational complexity, thus results into poor-scalability of the optimization algorithm from practical pers

Sparse Representation  Modeling and Learning in Visual Recognition Book

Sparse Representation Modeling and Learning in Visual Recognition


  • Author : Hong Cheng
  • Publisher : Springer
  • Release Date : 2015-05-25
  • Genre: Computers
  • Pages : 257
  • ISBN 10 : 9781447167143

DOWNLOAD BOOK
Sparse Representation Modeling and Learning in Visual Recognition Excerpt :

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

Statistical Learning with Sparsity Book

Statistical Learning with Sparsity


  • Author : Trevor Hastie
  • Publisher : CRC Press
  • Release Date : 2015-05-07
  • Genre: Business & Economics
  • Pages : 367
  • ISBN 10 : 9781498712170

DOWNLOAD BOOK
Statistical Learning with Sparsity Excerpt :

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.