Deep Learning through Sparse and Low Rank Modeling

Written By Zhangyang Wang
Deep Learning through Sparse and Low Rank Modeling
  • Publsiher : Academic Press
  • Release : 15 May 2019
  • ISBN : 0128136596
  • Pages : 300 pages
  • Rating : 4/5 from 21 reviews
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Read or download book entitled Deep Learning through Sparse and Low Rank Modeling written by Zhangyang Wang which was release on 15 May 2019, this book published by Academic Press. Available in PDF, EPUB and Kindle Format. Book excerpt: 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 Date : 2019-05-15
  • Total pages : 300
  • ISBN : 0128136596
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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/...

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
  • Total pages : 133
  • ISBN : 0128136596
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Summary : Mots-clés de l'auteur: automatic speech recognition ; deep neural network ; sparsity ; dictionary learning ; low-rank ; principal component analysis ; far-field speech ; information theory....

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
  • Total pages : 236
  • ISBN : 0128136596
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Summary : 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, ...

Spectral Geometry of Shapes

Spectral Geometry of Shapes
  • Author : Jing Hua,Zichun Zhong
  • Publisher : Academic Press
  • Release Date : 2020-01-15
  • Total pages : 195
  • ISBN : 0128136596
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Summary : 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 ...

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
  • Total pages : 520
  • ISBN : 0128136596
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Summary : 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
  • Release Date : 2016-05-03
  • Total pages : 142
  • ISBN : 0128136596
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Summary : 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, ...

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
  • Total pages : 144
  • ISBN : 0128136596
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Summary : 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, ...

Sparse Representation Modeling and Learning in Visual Recognition

Sparse Representation  Modeling and Learning in Visual Recognition
  • Author : Hong Cheng
  • Publisher : Springer
  • Release Date : 2015-05-25
  • Total pages : 257
  • ISBN : 0128136596
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Summary : 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 ...

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
  • Total pages : 291
  • ISBN : 0128136596
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Summary : 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 ...

Pattern Recognition And Big Data

Pattern Recognition And Big Data
  • Author : Pal Sankar Kumar,Pal Amita
  • Publisher : World Scientific
  • Release Date : 2016-12-15
  • Total pages : 876
  • ISBN : 0128136596
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Summary : 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 ...

Algorithmic Aspects of Machine Learning

Algorithmic Aspects of Machine Learning
  • Author : Ankur Moitra
  • Publisher : Cambridge University Press
  • Release Date : 2018-09-27
  • Total pages : 176
  • ISBN : 0128136596
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Summary : Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit....

Machine Learning ECML

Machine Learning  ECML
  • Author : Anonim
  • Publisher : Unknown
  • Release Date : 2004
  • Total pages : 212
  • ISBN : 0128136596
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Summary : Download or read online Machine Learning ECML written by , published by which was released on 2004. Get Machine Learning ECML Books now! Available in PDF, ePub and Kindle....

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 Date : 2020-12-31
  • Total pages : 204
  • ISBN : 0128136596
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Summary : 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 ...

Neural Information Processing

Neural Information Processing
  • Author : Tom Gedeon,Kok Wai Wong,Minho Lee
  • Publisher : Springer Nature
  • Release Date : 2019-12-10
  • Total pages : 709
  • ISBN : 0128136596
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Summary : The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of ...

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
  • Author : Emmanuel Vincent,Arie Yeredor,Zbyněk Koldovský,Petr Tichavský
  • Publisher : Springer
  • Release Date : 2015-08-14
  • Total pages : 532
  • ISBN : 0128136596
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Summary : This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special ...