Low Rank Approximation

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Low Rank Approximation

Author : Ivan Markovsky
ISBN : 9783319896205
Genre : Technology & Engineering
File Size : 49.31 MB
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This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required. The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of: • variable projection for structured low-rank approximation;• missing data estimation;• data-driven filtering and control;• stochastic model representation and identification;• identification of polynomial time-invariant systems; and• blind identification with deterministic input model. The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB^® /Octave examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis. “Each chapter is completed with a new section of exercises to which complete solutions are provided.” Low-Rank Approximation (second edition) is a broad survey of the Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.
Category: Technology & Engineering

Machine Learning Low Rank Approximations And Reduced Order Modeling In Computational Mechanics

Author : Felix Fritzen
ISBN : 9783039214099
Genre : Technology & Engineering
File Size : 25.89 MB
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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.
Category: Technology & Engineering

Low Rank Approximation Techniques For Graph Based Clustering

Author : Genís Floriach Pigem
ISBN : OCLC:1224074033
Genre :
File Size : 69.86 MB
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Clustering analysis is one of the main tools for exploratory data analysis, with applications from statistics, image processing, biology to social sciences. Generally, it isue data process to and meaning ful structure, explanatory underlying processes and generative features. Its goal is to group set of objects in such a way that objects in the same group (clusters) are similar to each other (insomesense) whilst objects from different clusters are dissimilar. One way to perform clustering analysis is to look at data asagraph, then clustering becomes a graph cutting problem. Within this set of techniques, Spectral Clustering stands for its simplicity and great performance. Recently, a new approach [12] radically diferent from spectral clustering has emerged and it consists on learning agraph that has the desired properties, namely, that it has the desired number of connected components or clusters. In this thesis we have explored these techniques and we have proposed new ones with the goal of designing an eficient algorithm that can exploit the additional information in directed graphs, with respect to achieve good clustering performance. Experimental results on synthetic data sets exhibitthe efectiveness of the proposed methods.

Low Rank Approximations Of Nonseparable Panel Models

Author : Iván Fernández-Val
ISBN : OCLC:1225947082
Genre :
File Size : 33.46 MB
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We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-difference approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.

Deep Learning Through Sparse And Low Rank Modeling

Author : Zhangyang Wang
ISBN : 9780128136591
Genre : Computers
File Size : 88.94 MB
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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/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
Category: Computers

Electromagnetic Nondestructive Evaluation Xxii

Author : A. Tamburrino
ISBN : 9781643680415
Genre : Technology & Engineering
File Size : 20.76 MB
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The use of electromagnetic nondestructive evaluation has grown significantly in recent years. This valuable technique enables the assessment of objects by observing the electromagnetic response to electric currents and/or magnetic fields introduced within them. This book presents the proceedings of the 23rd International Workshop on Electromagnetic Nondestructive Evaluation (ENDE2018), held in Detroit, Michigan, USA, from 9 - 13 September 2018. The workshop provides an international forum for the exchange of information on state-of-the-art technologies and development in electromagnetic nondestructive evaluation, and the 19 papers presented here cover topics including sensors; modeling; signal processing; inverse problems; materials state awareness and characterization; damage diagnosis and prognosis; biomedical applications; and innovative industrial applications of eNDE. Providing a comprehensive overview of current theoretical and applied research into electromagnetic nondestructive evaluation (eNDE) methods, the book will be of interest to all those whose work involves the non-destructive evaluation of objects, whatever their field.
Category: Technology & Engineering

Intelligent Systems And Computer Technology

Author : D.J. Hemanth
ISBN : 9781643681030
Genre : Computers
File Size : 49.68 MB
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Recent developments in soft-computation techniques have paved the way for handling huge volumes of data, thereby bringing about significant changes and technological advancements. This book presents the proceedings of the 3rd International Conference on Emerging Current Trends in Computing & Expert Technology (COMET 2020), held at Panimalar Engineering College, Chennai, India on 6 and 7 March 2020. The aim of the book is to disseminate cutting-edge developments taking place in the technological fields of intelligent systems and computer technology, thereby assisting researchers and practitioners from both institutions and industry to upgrade their knowledge of the latest developments and emerging areas of study. It focuses on technological innovations and trendsetting initiatives to improve business values, optimize business processes and enable inclusive growth for corporates, industries and education alike. The book is divided into two sections; ‘Next Generation Soft Computing’ is a platform for scientists, researchers, practitioners and academics to present and discuss their most recent innovations, trends and concerns, as well as the practical challenges encountered in the field. The second section, ‘Evolutionary Networking and Communications’ focuses on various aspects of 5G communications systems and networking, including cloud and virtualization solutions, management technologies, and vertical application areas. It brings together the latest technologies from all over the world, and also provides an excellent international forum for the sharing of knowledge and results from theory, methodology and applications in networking and communications. The book will be of interest to all those working in the fields of intelligent systems and computer technology.
Category: Computers

Low Rank And Sparse Modeling For Visual Analysis

Author : Yun Fu
ISBN : 9783319120003
Genre : Computers
File Size : 90.36 MB
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This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, 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.
Category: Computers

Intelligent Computing In Signal Processing And Pattern Recognition

Author : De-Shuang Huang
ISBN : UOM:39015069127531
Genre : Computers
File Size : 77.14 MB
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This 1179-page book assembles the complete contributions to the International Conference on Intelligent Computing, ICIC 2006: one volume of Lecture Notes in Computer Science (LNCS); one of Lecture Notes in Artificial Intelligence (LNAI); one of Lecture Notes in Bioinformatics (LNBI); and two volumes of Lecture Notes in Control and Information Sciences (LNCIS). Include are 149 revised full papers, and a Special Session on Computing for Searching Strategies to Control Dynamic Processes.
Category: Computers


Author :
ISBN : UOM:39015048217015
Genre : Computers
File Size : 64.17 MB
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Category: Computers

Statistical Data Analysis And Inference

Author : Yadolah Dodge
ISBN : UCAL:B4405460
Genre : Mathematics
File Size : 34.87 MB
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A wide range of topics and perspectives in the field of statistics are brought together in this volume. The contributions originate from invited papers presented at an international conference which was held in honour of C. Radhakrishna Rao, one of the most eminent statisticians of our time and a distinguished scientist.
Category: Mathematics