Sparse Data Representation The Role Of Redundancy In Data Processing

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Sparse Data Representation: The Role of Redundancy in Data Processing

Author :
Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (644 download)

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Book Synopsis Sparse Data Representation: The Role of Redundancy in Data Processing by :

Download or read book Sparse Data Representation: The Role of Redundancy in Data Processing written by and published by . This book was released on 2005 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last decade has seen a dramatic increase in computational power and sensor ubiquity, as well as an ever increasing demand for finer resolution in both scientific and geometric modeling. This has led to the creation of enormously large data sets with exquisite detail. However, these data sets will be useful only if we can process them efficiently, whether it be for storage, transmission, visual display, fast on-line graphical query, correlation, or registration against data from other modalities. Raw data sets are typically inaccessible and need to be transformed to more efficient representations for further processing. Several competing issues emerge. Sparsity is essential for efficient transmission, storage, and computation. Multiscale representations are critical to extract features at desired scales. Implementation in silicon leads to new issues of robustness in the face of computational error and imprecise circuit implementation. An emerging technology to address these issues utilizes redundant representations. High oversampling followed by coarse quantization is the preferred method for analog to digital conversion of signals. Sparse representation of images using redundant families of waveforms is effectively utilized in feature extraction and denoising. These redundant families can be frames, dictionaries, or libraries of bases. On the other hand, there is, at present, no compelling theory to explain the advantages of redundancy in image and signal processing. This program will convene leading experts from data representation into two workshops to describe the current understanding of the benefits of redundancy and to set forward a program for further research.

Sparse and Redundant Representations

Author : Michael Elad
Publisher : Springer Science & Business Media
ISBN 13 : 1441970118
Total Pages : 376 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Sparse and Redundant Representations by : Michael Elad

Download or read book Sparse and Redundant Representations written by Michael Elad and published by Springer Science & Business Media. This book was released on 2010-08-12 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.

Digital Signal Processing with Kernel Methods

Author : Jose Luis Rojo-Alvarez
Publisher : John Wiley & Sons
ISBN 13 : 1118611799
Total Pages : 665 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Digital Signal Processing with Kernel Methods by : Jose Luis Rojo-Alvarez

Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Semantics in Data and Knowledge Bases

Author : Klaus-Dieter Schewe
Publisher : Springer Science & Business Media
ISBN 13 : 3642234402
Total Pages : 142 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Semantics in Data and Knowledge Bases by : Klaus-Dieter Schewe

Download or read book Semantics in Data and Knowledge Bases written by Klaus-Dieter Schewe and published by Springer Science & Business Media. This book was released on 2011-09-06 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Semantics in Data and Knowledge Bases, SDKB 2010, held in Bordeaux, France in July 2010. The 6 revised full papers presented together with an introductory survey by the volume editors were carefully reviewed and selected during two rounds of revision and improvement. The papers reflect a variety of approaches to semantics in data and knowledge bases.

Finite Frames

Author : Peter G. Casazza
Publisher : Springer Science & Business Media
ISBN 13 : 0817683720
Total Pages : 492 pages
Book Rating : 4.8/5 (176 download)

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Book Synopsis Finite Frames by : Peter G. Casazza

Download or read book Finite Frames written by Peter G. Casazza and published by Springer Science & Business Media. This book was released on 2012-09-13 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hilbert space frames have long served as a valuable tool for signal and image processing due to their resilience to additive noise, quantization, and erasures, as well as their ability to capture valuable signal characteristics. More recently, finite frame theory has grown into an important research topic in its own right, with a myriad of applications to pure and applied mathematics, engineering, computer science, and other areas. The number of research publications, conferences, and workshops on this topic has increased dramatically over the past few years, but no survey paper or monograph has yet appeared on the subject. Edited by two of the leading experts in the field, Finite Frames aims to fill this void in the literature by providing a comprehensive, systematic study of finite frame theory and applications. With carefully selected contributions written by highly experienced researchers, it covers topics including: * Finite Frame Constructions; * Optimal Erasure Resilient Frames; * Quantization of Finite Frames; * Finite Frames and Compressed Sensing; * Group and Gabor Frames; * Fusion Frames. Despite the variety of its chapters' source and content, the book's notation and terminology are unified throughout and provide a definitive picture of the current state of frame theory. With a broad range of applications and a clear, full presentation, this book is a highly valuable resource for graduate students and researchers across disciplines such as applied harmonic analysis, electrical engineering, quantum computing, medicine, and more. It is designed to be used as a supplemental textbook, self-study guide, or reference book.

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

Author : Mr. Srinivas Rao Adabala
Publisher : Xoffencerpublication
ISBN 13 : 8119534174
Total Pages : 207 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION by : Mr. Srinivas Rao Adabala

Download or read book DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION written by Mr. Srinivas Rao Adabala and published by Xoffencerpublication. This book was released on 2023-08-14 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.

Advanced Hybrid Information Processing

Author : Weina Fu
Publisher : Springer Nature
ISBN 13 : 3031287878
Total Pages : 806 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Advanced Hybrid Information Processing by : Weina Fu

Download or read book Advanced Hybrid Information Processing written by Weina Fu and published by Springer Nature. This book was released on 2023-03-21 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the post-conference proceedings of the 6th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2022, held in Changsha, China, in September 29-30, 2022. The 109 full papers presented were selected from 276 submissions and focus on theory and application of hybrid information processing technology for smarter and more effective research and application. The theme of ADHIP 2022 was Hybrid Information Processing in Meta World. The papers are named in topical sections as follows: Information Extracting and Processing in Digital World; Education Based methods in Learning and Teaching; Various Systems for Digital World.

Academic Press Library in Signal Processing

Author : Mats Viberg
Publisher : Academic Press
ISBN 13 : 0124116213
Total Pages : 1013 pages
Book Rating : 4.1/5 (241 download)

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Book Synopsis Academic Press Library in Signal Processing by : Mats Viberg

Download or read book Academic Press Library in Signal Processing written by Mats Viberg and published by Academic Press. This book was released on 2013-08-31 with total page 1013 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Advanced Hybrid Information Processing

Author : Lin Yun
Publisher : Springer Nature
ISBN 13 : 3031505492
Total Pages : 480 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Advanced Hybrid Information Processing by : Lin Yun

Download or read book Advanced Hybrid Information Processing written by Lin Yun and published by Springer Nature. This book was released on with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Academic Press Library in Signal Processing, Volume 7

Author :
Publisher : Academic Press
ISBN 13 : 0128118881
Total Pages : 654 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Academic Press Library in Signal Processing, Volume 7 by :

Download or read book Academic Press Library in Signal Processing, Volume 7 written by and published by Academic Press. This book was released on 2017-12-01 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering is aimed at university researchers, post graduate students and R&D engineers in the industry, providing a tutorial-based, comprehensive review of key topics and technologies of research in Array and Radar Processing, Communications Engineering and Machine Learning. Users will find the book to be an invaluable starting point to their research and initiatives. With this reference, readers will quickly grasp an unfamiliar area of research, understand the underlying principles of a topic, learn how a topic relates to other areas, and learn of research issues yet to be resolved. Presents a quick tutorial of reviews of important and emerging topics of research Explores core principles, technologies, algorithms and applications Edited and contributed by international leading figures in the field Includes comprehensive references to journal articles and other literature upon which to build further, more detailed knowledge

Machine Learning Techniques on Gene Function Prediction Volume II

Author : Quan Zou
Publisher : Frontiers Media SA
ISBN 13 : 2889766322
Total Pages : 264 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Machine Learning Techniques on Gene Function Prediction Volume II by : Quan Zou

Download or read book Machine Learning Techniques on Gene Function Prediction Volume II written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Intelligent Data Analysis XVII

Author : Wouter Duivesteijn
Publisher : Springer
ISBN 13 : 3030017680
Total Pages : 403 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Advances in Intelligent Data Analysis XVII by : Wouter Duivesteijn

Download or read book Advances in Intelligent Data Analysis XVII written by Wouter Duivesteijn and published by Springer. This book was released on 2018-10-10 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the conference proceedings of the 17th International Symposium on Intelligent Data Analysis, which was held in October 2018 in ‘s-Hertogenbosch, the Netherlands. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The 29 full papers presented in this book were carefully reviewed and selected from 65 submissions. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems.

Issues & Trends of Information Technology Management in Contemporary Organizations

Author : Information Resources Management Association. International Conference
Publisher : IGI Global
ISBN 13 : 9781930708396
Total Pages : 878 pages
Book Rating : 4.7/5 (83 download)

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Book Synopsis Issues & Trends of Information Technology Management in Contemporary Organizations by : Information Resources Management Association. International Conference

Download or read book Issues & Trends of Information Technology Management in Contemporary Organizations written by Information Resources Management Association. International Conference and published by IGI Global. This book was released on 2002-01-01 with total page 878 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the field of information technology continues to grow and expand, it impacts more and more organizations worldwide. The leaders within these organizations are challenged on a continuous basis to develop and implement programs that successfully apply information technology applications. This is a collection of unique perspectives on the issues surrounding IT in organizations and the ways in which these issues are addressed. This valuable book is a compilation of the latest research in the area of IT utilization and management.

High-Dimensional Data Analysis with Low-Dimensional Models

Author : John Wright
Publisher : Cambridge University Press
ISBN 13 : 1108489737
Total Pages : 717 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis High-Dimensional Data Analysis with Low-Dimensional Models by : John Wright

Download or read book High-Dimensional Data Analysis with Low-Dimensional Models written by John Wright and published by Cambridge University Press. This book was released on 2022-01-13 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.

Image Understanding using Sparse Representations

Author : Jayaraman J. Thiagarajan
Publisher : Springer Nature
ISBN 13 : 3031022505
Total Pages : 115 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Image Understanding using Sparse Representations by : Jayaraman J. Thiagarajan

Download or read book Image Understanding using Sparse Representations written by Jayaraman J. Thiagarajan and published by Springer Nature. This book was released on 2022-06-01 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.

e-Learning, e-Education, and Online Training

Author : Guan Gui
Publisher : Springer Nature
ISBN 13 : 3031514718
Total Pages : 487 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis e-Learning, e-Education, and Online Training by : Guan Gui

Download or read book e-Learning, e-Education, and Online Training written by Guan Gui and published by Springer Nature. This book was released on 2024-01-16 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume set constitutes the post-conference proceedings of the 9th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2023, held in Yantai, China, during August 17-18, 2023. The 104 full papers presented were selected from 260 submissions. The papers reflect the evolving landscape of education in the digital age. They were organized in topical sections as follows: IT promoted teaching platforms and systems; AI based educational modes and methods; automatic educational resource processing; educational information evaluation.

Applied Biclustering Methods for Big and High-Dimensional Data Using R

Author : Adetayo Kasim
Publisher : CRC Press
ISBN 13 : 1482208245
Total Pages : 428 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Applied Biclustering Methods for Big and High-Dimensional Data Using R by : Adetayo Kasim

Download or read book Applied Biclustering Methods for Big and High-Dimensional Data Using R written by Adetayo Kasim and published by CRC Press. This book was released on 2016-10-03 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.