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 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

Produk Detail:

  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Pages : 300 pages
  • ISBN : 0128136596
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKDeep Learning through Sparse and Low Rank Modeling

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 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 : 11 April 2019
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

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

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

Spectral Geometry of Shapes

Spectral Geometry of Shapes
  • Author : Jing Hua,Zichun Zhong
  • Publisher : Academic Press
  • Release : 15 January 2020
GET THIS BOOKSpectral 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

Generalized Low Rank Models

Generalized Low Rank Models
  • Author : Madeleine Udell,Corinne Horn,Reza Zadeh,Stephen Boyd
  • Publisher : Unknown Publisher
  • Release : 03 May 2016
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

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 : 16 October 2019
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

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 : 20 September 2016
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 : 30 October 2014
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.

Machine Learning

Machine Learning
  • Author : Sergios Theodoridis
  • Publisher : Academic Press
  • Release : 19 February 2020
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

Advances in Electric Power and Energy

Advances in Electric Power and Energy
  • Author : Mohamed E. El-Hawary
  • Publisher : John Wiley & Sons
  • Release : 08 December 2020
GET THIS BOOKAdvances 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

Pattern Recognition And Big Data

Pattern Recognition And Big Data
  • Author : Pal Sankar Kumar,Pal Amita
  • Publisher : World Scientific
  • Release : 15 December 2016
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

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

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
  • Author : Felix Fritzen,David Ryckelynck
  • Publisher : MDPI
  • Release : 18 September 2019
GET THIS BOOKMachine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
  • Author : Danail Stoyanov,Zeike Taylor,Gustavo Carneiro,Tanveer Syeda-Mahmood,Anne Martel,Lena Maier-Hein,João Manuel R.S. Tavares,Andrew Bradley,João Paulo Papa,Vasileios Belagiannis,Jacinto C. Nascimento,Zhi Lu,Sailesh Conjeti,Mehdi Moradi,Hayit Greenspan,Anant Madabhushi
  • Publisher : Springer
  • Release : 19 September 2018
GET THIS BOOKDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The

Low-Rank Models in Visual Analysis

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

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Chunfeng Lian,Xiaohuan Cao,Islem Rekik,Xuanang Xu,Pingkun Yan
  • Publisher : Springer Nature
  • Release : 25 September 2021
GET THIS BOOKMachine 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