Deep Learning through Sparse and Low Rank Modeling

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  • Publisher : Academic Press
  • Release : 15 May 2019
  • ISBN : 9780128136591
  • Page : 300 pages
  • Rating : 4.5/5 from 103 voters

Download Deep Learning through Sparse and Low Rank Modeling in PDF, Epub and Kindle

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

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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher : Academic Press
  • Release Date : 2019-05-15
  • ISBN : 9780128136591
GET THIS BOOKDeep 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-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

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher : Academic Press
  • Release Date : 2019-04-11
  • ISBN : 9780128136607
GET THIS BOOKDeep 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-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

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
  • Author : Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah
  • Publisher : CRC Press
  • Release Date : 2016-09-20
  • ISBN : 9781315353531
GET THIS BOOKHandbook 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 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

Low-Rank and Sparse Modeling for Visual Analysis

Low-Rank and Sparse Modeling for Visual Analysis
  • Author : Yun Fu
  • Publisher : Springer
  • Release Date : 2014-10-30
  • ISBN : 9783319120003
GET THIS BOOKLow-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 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.

Pattern Recognition And Big Data

Pattern Recognition And Big Data
  • Author : Pal Sankar Kumar,Pal Amita
  • Publisher : World Scientific
  • Release Date : 2016-12-15
  • ISBN : 9789813144569
GET THIS BOOKPattern Recognition And Big Data

Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and

Inpainting and Denoising Challenges

Inpainting and Denoising Challenges
  • Author : Sergio Escalera,Stephane Ayache,Jun Wan,Meysam Madadi,Umut Güçlü,Xavier Baró
  • Publisher : Springer Nature
  • Release Date : 2019-10-16
  • ISBN : 9783030256142
GET THIS BOOKInpainting and Denoising Challenges

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

Generalized Low Rank Models

Generalized Low Rank Models
  • Author : Madeleine Udell,Corinne Horn,Reza Zadeh,Stephen Boyd
  • Publisher : Unknown
  • Release Date : 2016-05-03
  • ISBN : 1680831402
GET THIS BOOKGeneralized Low Rank Models

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

Sparse and Low-rank Modeling for Automatic Speech Recognition

Sparse and Low-rank Modeling for Automatic Speech Recognition
  • Author : Pranay Dighe
  • Publisher : Unknown
  • Release Date : 2019
  • ISBN : OCLC:1091605389
GET THIS BOOKSparse and Low-rank Modeling for Automatic Speech Recognition

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

Machine Learning

Machine Learning
  • Author : Sergios Theodoridis
  • Publisher : Academic Press
  • Release Date : 2020-02-19
  • ISBN : 9780128188040
GET THIS BOOKMachine Learning

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

Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis
  • Author : Zhouchen Lin,Hongyang Zhang
  • Publisher : Academic Press
  • Release Date : 2017-06-06
  • ISBN : 9780128127322
GET THIS BOOKLow-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 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

Feature Learning and Understanding

Feature Learning and Understanding
  • Author : Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang
  • Publisher : Springer Nature
  • Release Date : 2020-04-03
  • ISBN : 9783030407940
GET THIS BOOKFeature Learning and Understanding

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction,

Artificial Intelligence, Evolutionary Computing and Metaheuristics

Artificial Intelligence, Evolutionary Computing and Metaheuristics
  • Author : Xin-She Yang
  • Publisher : Springer
  • Release Date : 2012-07-27
  • ISBN : 9783642296949
GET THIS BOOKArtificial Intelligence, Evolutionary Computing and Metaheuristics

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

Computer Vision – ECCV 2012

Computer Vision – ECCV 2012
  • Author : Andrew Fitzgibbon,Svetlana Lazebnik,Pietro Perona,Yoichi Sato,Cordelia Schmid
  • Publisher : Springer
  • Release Date : 2012-09-26
  • ISBN : 9783642337833
GET THIS BOOKComputer Vision – ECCV 2012

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image

Hands-On Machine Learning with R

Hands-On Machine Learning with R
  • Author : Brad Boehmke,Brandon M. Greenwell
  • Publisher : CRC Press
  • Release Date : 2019-11-07
  • ISBN : 9781000730197
GET THIS BOOKHands-On Machine Learning with R

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

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
  • Author : Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah
  • Publisher : CRC Press
  • Release Date : 2016-09-20
  • ISBN : 9781498724630
GET THIS BOOKHandbook 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 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