Deep Learning through Sparse and Low Rank Modeling

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  • 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

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 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.

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 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

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 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

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 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

Sparse Representation Modeling and Learning in Visual Recognition

Sparse Representation  Modeling and Learning in Visual Recognition
  • Author : Hong Cheng
  • Publisher : Springer
  • Release : 25 May 2015
GET THIS BOOK Sparse Representation Modeling and Learning in Visual Recognition

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,

Study on Efficient Sparse and Low rank Optimization and Its Applications

Study on Efficient Sparse and Low rank Optimization and Its Applications
  • Author : Jian Lou
  • Publisher : Unknown
  • Release : 22 June 2021
GET THIS BOOK Study on Efficient Sparse and Low rank Optimization and Its Applications

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,

Robust Representation for Data Analytics

Robust Representation for Data Analytics
  • Author : Sheng Li,Yun Fu
  • Publisher : Springer
  • Release : 09 August 2017
GET THIS BOOK Robust Representation for Data Analytics

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning,

Learning Representation for Multi View Data Analysis

Learning Representation for Multi View Data Analysis
  • Author : Zhengming Ding,Handong Zhao,Yun Fu
  • Publisher : Springer
  • Release : 06 December 2018
GET THIS BOOK Learning Representation for Multi View Data Analysis

This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot

Low Rank and Sparse Modeling for Data Analysis

Low Rank and Sparse Modeling for Data Analysis
  • Author : Zhao Kang
  • Publisher : Unknown
  • Release : 22 June 2021
GET THIS BOOK Low Rank and Sparse Modeling for Data Analysis

Nowadays, many real-world problems must deal with collections of high-dimensional data. High dimensional data usually have intrinsic low-dimensional representations, which are suited for subsequent analysis or processing. Therefore, finding low-dimensional representations is an essential step in many machine learning and data mining tasks. Low-rank and sparse modeling are emerging mathematical tools dealing with uncertainties of real-world data. Leveraging on the underlying structure of data, low-rank and sparse modeling approaches have achieved impressive performance in many data analysis tasks. Since the

Feature Learning and Understanding

Feature Learning and Understanding
  • Author : Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang
  • Publisher : Springer Nature
  • Release : 03 April 2020
GET THIS BOOK Feature 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,

Separation of Singing Voice from Music Using Extended Robust Principle Component Analysis and Deep Learning

Separation of Singing Voice from Music Using Extended Robust Principle Component Analysis and Deep Learning
  • Author : Feng Li
  • Publisher : Scientific Research Publishing, Inc. USA
  • Release : 31 December 2020
GET THIS BOOK Separation of Singing Voice from Music Using Extended Robust Principle Component Analysis and Deep Learning

This book proposes two extensions of the effective optimization algorithms concentrating on RPCA and Fusion-Net for singing voice separation. One is using different weighted value for describing the separated low-rank matrix. The other is exploring rank-1 constraint minimization of singular value in RPCA. In terms of source-to-artifact ratio, the previous is better than the later. However, CRPCA obtains better separation quality than WRPCA in singing voice separation. The outcomes of this research contribute to further improving the technologies related to