Practical Applications Of Sparse Modeling

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Practical Applications of Sparse Modeling

Author : Irina Rish
Publisher : MIT Press
ISBN 13 : 0262027720
Total Pages : 265 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Practical Applications of Sparse Modeling by : Irina Rish

Download or read book Practical Applications of Sparse Modeling written by Irina Rish and published by MIT Press. This book was released on 2014-09-12 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models"--Jacket.

Sparse Modeling

Author : Irina Rish
Publisher : CRC Press
ISBN 13 : 1439828709
Total Pages : 250 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Sparse Modeling by : Irina Rish

Download or read book Sparse Modeling written by Irina Rish and published by CRC Press. This book was released on 2014-12-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing.Sparse Modeling: Theory, Algorithms, and Applications provides an introduction t

Sparse Modeling

Author : Irina Rish
Publisher : CRC Press
ISBN 13 : 1439828695
Total Pages : 255 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Sparse Modeling by : Irina Rish

Download or read book Sparse Modeling written by Irina Rish and published by CRC Press. This book was released on 2014-12-01 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.

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.

Machine Learning and Interpretation in Neuroimaging

Author : Irina Rish
Publisher : Springer
ISBN 13 : 331945174X
Total Pages : 133 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Machine Learning and Interpretation in Neuroimaging by : Irina Rish

Download or read book Machine Learning and Interpretation in Neuroimaging written by Irina Rish and published by Springer. This book was released on 2016-09-12 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers from the 4th International Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2014, held in Montreal, QC, Canada, in December 2014 as a satellite event of the 11th annual conference on Neural Information Processing Systems, NIPS 2014. The 10 MLINI 2014 papers presented in this volume were carefully reviewed and selected from 17 submissions. They were organized in topical sections named: networks and decoding; speech; clinics and cognition; and causality and time-series. In addition, the book contains the 3 best papers presented at MLINI 2013.

Encyclopedia of Computer Science and Technology

Author : Phillip A. Laplante
Publisher : CRC Press
ISBN 13 : 1351652494
Total Pages : 1013 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Encyclopedia of Computer Science and Technology by : Phillip A. Laplante

Download or read book Encyclopedia of Computer Science and Technology written by Phillip A. Laplante and published by CRC Press. This book was released on 2017-10-02 with total page 1013 pages. Available in PDF, EPUB and Kindle. Book excerpt: With breadth and depth of coverage, the Encyclopedia of Computer Science and Technology, Second Edition has a multi-disciplinary scope, drawing together comprehensive coverage of the inter-related aspects of computer science and technology. The topics covered in this encyclopedia include: General and reference Hardware Computer systems organization Networks Software and its engineering Theory of computation Mathematics of computing Information systems Security and privacy Human-centered computing Computing methodologies Applied computing Professional issues Leading figures in the history of computer science The encyclopedia is structured according to the ACM Computing Classification System (CCS), first published in 1988 but subsequently revised in 2012. This classification system is the most comprehensive and is considered the de facto ontological framework for the computing field. The encyclopedia brings together the information and historical context that students, practicing professionals, researchers, and academicians need to have a strong and solid foundation in all aspects of computer science and technology.

Low-Rank and Sparse Modeling for Visual Analysis

Author : Yun Fu
Publisher : Springer
ISBN 13 : 331912000X
Total Pages : 240 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Low-Rank and Sparse Modeling for Visual Analysis by : Yun Fu

Download or read book Low-Rank and Sparse Modeling for Visual Analysis written by Yun Fu and published by Springer. This book was released on 2014-10-30 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Log-Linear Models, Extensions, and Applications

Author : Aleksandr Aravkin
Publisher : MIT Press
ISBN 13 : 0262039508
Total Pages : 215 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Log-Linear Models, Extensions, and Applications by : Aleksandr Aravkin

Download or read book Log-Linear Models, Extensions, and Applications written by Aleksandr Aravkin and published by MIT Press. This book was released on 2018-11-27 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications. Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives. Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg

Compressed Sensing

Author : Yonina C. Eldar
Publisher : Cambridge University Press
ISBN 13 : 1107394392
Total Pages : 557 pages
Book Rating : 4.1/5 (73 download)

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Book Synopsis Compressed Sensing by : Yonina C. Eldar

Download or read book Compressed Sensing written by Yonina C. Eldar and published by Cambridge University Press. This book was released on 2012-05-17 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

Handbook of Dynamic Data Driven Applications Systems

Author : Frederica Darema
Publisher : Springer Nature
ISBN 13 : 3031279867
Total Pages : 937 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Handbook of Dynamic Data Driven Applications Systems by : Frederica Darema

Download or read book Handbook of Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2023-10-16 with total page 937 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).

Machine Learning and Knowledge Discovery in Databases

Author : Dimitrios Gunopulos
Publisher : Springer Science & Business Media
ISBN 13 : 3642237797
Total Pages : 678 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Dimitrios Gunopulos

Download or read book Machine Learning and Knowledge Discovery in Databases written by Dimitrios Gunopulos and published by Springer Science & Business Media. This book was released on 2011-09-06 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video

Author : Olga Isupova
Publisher : Springer
ISBN 13 : 3319755080
Total Pages : 144 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video by : Olga Isupova

Download or read book Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video written by Olga Isupova and published by Springer. This book was released on 2018-02-24 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.

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.

Deep Learning through Sparse and Low-Rank Modeling

Author : Zhangyang Wang
Publisher : Academic Press
ISBN 13 : 0128136596
Total Pages : 296 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang

Download or read book Deep Learning through Sparse and Low-Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-12 with total page 296 pages. Available in PDF, EPUB and Kindle. 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.

Advanced Structural Safety Studies

Author : Jeom Kee Paik
Publisher : Springer
ISBN 13 : 981138245X
Total Pages : 682 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Advanced Structural Safety Studies by : Jeom Kee Paik

Download or read book Advanced Structural Safety Studies written by Jeom Kee Paik and published by Springer. This book was released on 2019-07-25 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes principles, industry practices and evolutionary methodologies for advanced safety studies, which are helpful in effectively managing volatile, uncertain, complex, and ambiguous (VUCA) environments within the framework of quantitative risk assessment and management and associated with the safety and resilience of structures and infrastructures with tolerance against various types of extreme conditions and accidents such as fires, explosions, collisions and grounding. It presents advanced computational models for characterizing structural actions and their effects in extreme and accidental conditions, which are highly nonlinear and non-Gaussian in association with multiple physical processes, multiple scales, and multiple criteria. Probabilistic scenario selection practices and applications are presented. Engineering practices for structural crashworthiness analysis in extreme conditions and accidents are described. Multidisciplinary approaches involving advanced computational models and large-scale physical model testing are emphasized. The book will be useful to students at a post-graduate level as well as researchers and practicing engineers.

Advanced Structured Prediction

Author : Sebastian Nowozin
Publisher : MIT Press
ISBN 13 : 0262028379
Total Pages : 430 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Advanced Structured Prediction by : Sebastian Nowozin

Download or read book Advanced Structured Prediction written by Sebastian Nowozin and published by MIT Press. This book was released on 2014-12-05 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

Perturbations, Optimization, and Statistics

Author : Tamir Hazan
Publisher : MIT Press
ISBN 13 : 0262337940
Total Pages : 412 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Perturbations, Optimization, and Statistics by : Tamir Hazan

Download or read book Perturbations, Optimization, and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2017-09-22 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.