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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling
A Book

by Zhangyang Wang,Yun Fu,Thomas S. Huang

  • Publisher : Academic Press
  • Release : 2019-05-15
  • Pages : 300
  • ISBN : 0128136596
  • Language : En, Es, Fr & De
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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/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
A Book

by Zhangyang Wang,Yun Fu,Thomas S. Huang

  • Publisher : Academic Press
  • Release : 2019-04-11
  • Pages : 296
  • ISBN : 012813660X
  • Language : En, Es, Fr & De
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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/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

Low-Rank and Sparse Modeling for Visual Analysis

Low-Rank and Sparse Modeling for Visual Analysis
A Book

by Yun Fu

  • Publisher : Springer
  • Release : 2014-10-30
  • Pages : 236
  • ISBN : 331912000X
  • Language : En, Es, Fr & De
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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, 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.

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging
Techniques and Applications

by Marcelo Bertalmío

  • Publisher : Academic Press
  • Release : 2019-11-06
  • Pages : 324
  • ISBN : 0128138955
  • Language : En, Es, Fr & De
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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 researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies. Presents the underlying vision science principles and models that are essential to the emerging technologies of HDR and WCG Explores state-of-the-art techniques for tone and gamut mapping Discusses open challenges and future directions of HDR and WCG research

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
A Book

by Mei Chen

  • Publisher : Academic Press
  • Release : 2020-12-01
  • Pages : 228
  • ISBN : 0128149736
  • Language : En, Es, Fr & De
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Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

Spectral Geometry of Shapes

Spectral Geometry of Shapes
A Book

by Jing Hua,Zichun Zhong

  • Publisher : Academic Press
  • Release : 2020-01-15
  • Pages : 195
  • ISBN : 0128138424
  • Language : En, Es, Fr & De
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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 has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource. Presents the latest advances in spectral geometric processing for 3D shape analysis applications, such as shape classification, shape matching, medical imaging, etc. Provides intuitive links between fundamental geometric theories and real-world applications, thus bridging the gap between theory and practice Describes new theoretical breakthroughs in applying spectral methods for non-isometric motion analysis Gives insights for developing spectral geometry-based approaches for 3D shape analysis and deep learning of shape geometry

Inpainting and Denoising Challenges

Inpainting and Denoising Challenges
A Book

by Sergio Escalera,Stephane Ayache,Jun Wan,Meysam Madadi,Umut Güçlü,Xavier Baró

  • Publisher : Springer Nature
  • Release : 2019-10-16
  • Pages : 144
  • ISBN : 3030256146
  • Language : En, Es, Fr & De
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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, 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 denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
Applications in Image and Video Processing

by Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah

  • Publisher : CRC Press
  • Release : 2016-09-20
  • Pages : 520
  • ISBN : 1498724639
  • Language : En, Es, Fr & De
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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 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 into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Advanced Data Analytics for Power Systems

Advanced Data Analytics for Power Systems
A Book

by Ali Tajer,Samir M. Perlaza,H. Vincent Poor

  • Publisher : Cambridge University Press
  • Release : 2021-01-31
  • Pages : 615
  • ISBN : 1108494757
  • Language : En, Es, Fr & De
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Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

Advances in Electric Power and Energy

Advances in Electric Power and Energy
Static State Estimation

by Mohamed E. El-Hawary

  • Publisher : John Wiley & Sons
  • Release : 2020-12-11
  • Pages : 512
  • ISBN : 1119480442
  • Language : En, Es, Fr & De
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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 threats to the integrity of their own and neighboring systems. The contributors provide an overview of the topic and review common threats such as cascading black-outs to model-based anomaly detection to the operation of micro-grids and much more. The book also includes a discussion of an effective mathematical programming approach to state estimation in power systems. Advances in Electric Power and Energy reviews the most recent developments in the field and: Offers an introduction to the topic to help non-experts (and professionals) get up-to-date on static state estimation Covers the essential information needed to understand power system state estimation written by experts on the subject Discusses a mathematical programming approach Written for electric power system planners, operators, consultants, power system software developers, and academics, Advances in Electric Power and Energy is the authoritative guide to the topic with contributions from experts who review the most recent developments.

Broad Learning Through Fusions

Broad Learning Through Fusions
An Application on Social Networks

by Jiawei Zhang,Philip S. Yu

  • Publisher : Springer
  • Release : 2019-06-08
  • Pages : 419
  • ISBN : 3030125289
  • Language : En, Es, Fr & De
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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 social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

PRICAI 2018: Trends in Artificial Intelligence

PRICAI 2018: Trends in Artificial Intelligence
15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, August 28–31, 2018, Proceedings

by Xin Geng,Byeong-Ho Kang

  • Publisher : Springer
  • Release : 2018-07-30
  • Pages : 1086
  • ISBN : 3319973045
  • Language : En, Es, Fr & De
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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.

Machine Learning Methods with Noisy, Incomplete or Small Datasets

Machine Learning Methods with Noisy, Incomplete or Small Datasets
A Book

by Jordi Solé-Casals,Zhe Sun,Cesar F. Caiafa,Toshihisa Tanaka

  • Publisher : MDPI
  • Release : 2021-08-17
  • Pages : 316
  • ISBN : 3036512888
  • Language : En, Es, Fr & De
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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 various risks, such as cyber risks, operational risks, regulatory risks, and others. Therefore, we believe that the entrepreneurial behavior and global mindset of decision-makers significantly contribute to the development of innovations, which benefit by closing the prevailing gap between developed and developing countries. Thus, this Special Issue contributes to closing the research gap in the literature by providing a platform for a scientific debate on innovation, internationalization and entrepreneurship, which would facilitate improving the resilience of businesses to future disruptions. Order Your Print Copy

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

by Chunfeng Lian

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 129
  • ISBN : 303087589X
  • Language : En, Es, Fr & De
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Multimedia and Network Information Systems

Multimedia and Network Information Systems
Proceedings of the 11th International Conference MISSI 2018

by Kazimierz Choroś,Marek Kopel,Elżbieta Kukla,Andrzej Siemiński

  • Publisher : Springer
  • Release : 2018-09-04
  • Pages : 599
  • ISBN : 3319986783
  • Language : En, Es, Fr & De
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These proceedings collect papers presented at the 11th International Conference on Multimedia & Network Information Systems (MISSI 2018), held from 12 to 14 September 2018 in Wrocław, Poland. The keynote lectures, given by four outstanding scientists, are also included here. The Conference attracted a great number of scientists from across Europe and beyond, and hosted the 6th International Workshop on Computational Intelligence for Multimedia Understanding as well as four special sessions. The majority of the papers describe various artificial intelligence (AI) methods applied to multimedia and natural language (NL) processing; they address hot topics such as virtual and augmented reality, identity recognition, video summarization, intelligent audio processing, accessing multilingual information and opinions, video games, and innovations in Web technologies. Accordingly, the proceedings provide a cutting-edge update on work being pursued in the rapidly evolving field of Multimedia and Internet Information Systems.

Big Data over Networks

Big Data over Networks
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 129
  • ISBN : 1107099005
  • Language : En, Es, Fr & De
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Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning
A Book

by Xin Liu,Anwitaman Datta,Ee-Peng Lim

  • Publisher : CRC Press
  • Release : 2014-10-29
  • Pages : 232
  • ISBN : 1482226669
  • Language : En, Es, Fr & De
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Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

Sparse and Low-rank Modeling for Automatic Speech Recognition

Sparse and Low-rank Modeling for Automatic Speech Recognition
A Book

by Pranay Dighe

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 133
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Mots-clés de l'auteur: automatic speech recognition ; deep neural network ; sparsity ; dictionary learning ; low-rank ; principal component analysis ; far-field speech ; information theory.

Pattern Recognition And Big Data

Pattern Recognition And Big Data
A Book

by Pal Sankar Kumar,Pal Amita

  • Publisher : World Scientific
  • Release : 2016-12-15
  • Pages : 876
  • ISBN : 9813144564
  • Language : En, Es, Fr & De
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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 granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Neural Information Processing

Neural Information Processing
24th International Conference, ICONIP 2017, Guangzhou, China, November 14–18, 2017, Proceedings

by Derong Liu,Shengli Xie,Yuanqing Li,Dongbin Zhao,El-Sayed M. El-Alfy

  • Publisher : Springer
  • Release : 2017-12-01
  • Pages : 912
  • ISBN : 3319701363
  • Language : En, Es, Fr & De
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The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.