Deep Learning through Sparse and Low-Rank Modeling
  • Author : Zhangyang Wang
  • Release Date : 26 April 2019
  • Publisher : Academic Press
  • Genre : Computers
  • Pages : 296
  • ISBN 13 : 9780128136591

Deep Learning through Sparse and Low-Rank Modeling Book Summary

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
Genre : Computers
Total View : 494 Views
File Size : 47,8 Mb
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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/...

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
Genre : Computers
Total View : 2402 Views
File Size : 44,9 Mb
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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 ...

Low Rank and Sparse Modeling for Visual Analysis

Low Rank and Sparse Modeling for Visual Analysis

Author : Yun Fu
Publisher : Springer
Genre : Computers
Total View : 6102 Views
File Size : 46,8 Mb
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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, ...

Inpainting and Denoising Challenges

Inpainting and Denoising Challenges

Author : Sergio Escalera,Stephane Ayache,Jun Wan,Meysam Madadi,Umut Güçlü,Xavier Baró
Publisher : Springer Nature
Genre : Computers
Total View : 9649 Views
File Size : 55,9 Mb
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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, ...

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
Genre : Computers
Total View : 7537 Views
File Size : 45,8 Mb
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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 ...

Generalized Low Rank Models

Generalized Low Rank Models

Author : Madeleine Udell,Corinne Horn,Reza Zadeh,Stephen Boyd
Publisher : Unknown
Genre : Uncategorized
Total View : 3832 Views
File Size : 40,8 Mb
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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, ...

Low Rank Models in Visual Analysis

Low Rank Models in Visual Analysis

Author : Zhouchen Lin,Hongyang Zhang
Publisher : Academic Press
Genre : Computers
Total View : 4502 Views
File Size : 50,8 Mb
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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 ...

Machine Learning

Machine Learning

Author : Sergios Theodoridis
Publisher : Academic Press
Genre : Computers
Total View : 969 Views
File Size : 48,7 Mb
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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, ...