Deep Learning through Sparse and Low Rank Modeling

Produk Detail:
  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Pages : 300 pages
  • ISBN : 0128136596
  • Rating : /5 from reviews
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Download or Read online Deep Learning through Sparse and Low Rank Modeling full in PDF, ePub and kindle. this book written by Zhangyang Wang and published by Academic Press which was released on 15 May 2019 with total page 300 pages. We cannot guarantee that Deep Learning through Sparse and Low Rank Modeling book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

Deep Learning through Sparse and Low Rank Modeling
  • Author : Zhangyang Wang,Yun Fu,Thomas S. Huang
  • Publisher : Academic Press
  • Release : 15 May 2019
GET THIS BOOK 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-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 : 11 April 2019
GET THIS BOOK 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-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

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  • Publisher : Academic Press
  • Release : 06 November 2019
GET THIS BOOK Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

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

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  • Publisher : Academic Press
  • Release : 15 January 2020
GET THIS BOOK Spectral Geometry of Shapes

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

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Low Rank and Sparse Modeling for Visual Analysis
  • Author : Yun Fu
  • Publisher : Springer
  • Release : 30 October 2014
GET THIS BOOK Low 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.

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Handbook of Robust Low Rank and Sparse Matrix Decomposition
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  • Publisher : CRC Press
  • Release : 20 September 2016
GET THIS BOOK Handbook 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

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  • Publisher : Springer
  • Release : 08 June 2019
GET THIS BOOK Broad Learning Through Fusions

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and

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  • Publisher : John Wiley & Sons
  • Release : 03 March 2021
GET THIS BOOK Advances in Electric Power and Energy

A guide to the role of static state estimation in the mitigation of potential system failures With contributions from a noted panel of experts on the topic, Advances in Electric Power and Energy: Static State Estimation addresses the wide-range of issues concerning static state estimation as a main energy control function and major tool for evaluating prevailing operating conditions in electric power systems worldwide. This book is an essential guide for system operators who must be fully aware of potential

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  • Publisher : Springer Nature
  • Release : 16 October 2019
GET THIS BOOK Inpainting 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

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  • Publisher : Springer
  • Release : 30 July 2018
GET THIS BOOK PRICAI 2018 Trends in Artificial Intelligence

This two-volume set, LNAI 11012 and 11013, constitutes the thoroughly refereed proceedings of the 15th Pacific Rim Conference on Artificial Intelligence, PRICAI 2018, held in Nanjing, China, in August 2018. The 82 full papers and 58 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics such as AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim.

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  • Publisher : MDPI
  • Release : 17 August 2021
GET THIS BOOK Machine Learning Methods with Noisy Incomplete or Small Datasets

Over the past years, businesses have had to tackle the issues caused by numerous forces from political, technological and societal environment. The changes in the global market and increasing uncertainty require us to focus on disruptive innovations and to investigate this phenomenon from different perspectives. The benefits of innovations are related to lower costs, improved efficiency, reduced risk, and better response to the customers’ needs due to new products, services or processes. On the other hand, new business models expose

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  • Publisher : Springer Nature
  • Release : 25 September 2021
GET THIS BOOK Machine Learning in Medical Imaging

This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold

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Low Rank Models in Visual Analysis
  • Author : Zhouchen Lin,Hongyang Zhang
  • Publisher : Academic Press
  • Release : 06 June 2017
GET THIS BOOK 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 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

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  • Release : 15 December 2016
GET THIS BOOK Pattern 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