Regularization Methods For Canonical Correlation Analysis Rank Correlation Matrices And Renyi Correlation Matrices

Download Regularization Methods For Canonical Correlation Analysis Rank Correlation Matrices And Renyi Correlation Matrices full books in PDF, epub, and Kindle. Read online Regularization Methods For Canonical Correlation Analysis Rank Correlation Matrices And Renyi Correlation Matrices ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

A Dynamical Approach to Random Matrix Theory

Author : László Erdős
Publisher : American Mathematical Soc.
ISBN 13 : 1470436485
Total Pages : 239 pages
Book Rating : 4.4/5 (74 download)

DOWNLOAD NOW!

Book Synopsis A Dynamical Approach to Random Matrix Theory by : László Erdős

Download or read book A Dynamical Approach to Random Matrix Theory written by László Erdős and published by American Mathematical Soc.. This book was released on 2017-08-30 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A co-publication of the AMS and the Courant Institute of Mathematical Sciences at New York University This book is a concise and self-contained introduction of recent techniques to prove local spectral universality for large random matrices. Random matrix theory is a fast expanding research area, and this book mainly focuses on the methods that the authors participated in developing over the past few years. Many other interesting topics are not included, and neither are several new developments within the framework of these methods. The authors have chosen instead to present key concepts that they believe are the core of these methods and should be relevant for future applications. They keep technicalities to a minimum to make the book accessible to graduate students. With this in mind, they include in this book the basic notions and tools for high-dimensional analysis, such as large deviation, entropy, Dirichlet form, and the logarithmic Sobolev inequality. This manuscript has been developed and continuously improved over the last five years. The authors have taught this material in several regular graduate courses at Harvard, Munich, and Vienna, in addition to various summer schools and short courses. Titles in this series are co-published with the Courant Institute of Mathematical Sciences at New York University.

High-Dimensional Probability

Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)

DOWNLOAD NOW!

Book Synopsis High-Dimensional Probability by : Roman Vershynin

Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Nonnegative Matrix and Tensor Factorizations

Author : Andrzej Cichocki
Publisher : John Wiley & Sons
ISBN 13 : 9780470747285
Total Pages : 500 pages
Book Rating : 4.7/5 (472 download)

DOWNLOAD NOW!

Book Synopsis Nonnegative Matrix and Tensor Factorizations by : Andrzej Cichocki

Download or read book Nonnegative Matrix and Tensor Factorizations written by Andrzej Cichocki and published by John Wiley & Sons. This book was released on 2009-07-10 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area. Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms. Provides a comparative analysis of the different methods in order to identify approximation error and complexity. Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book. The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.

Financial Signal Processing and Machine Learning

Author : Ali N. Akansu
Publisher : John Wiley & Sons
ISBN 13 : 1118745639
Total Pages : 312 pages
Book Rating : 4.1/5 (187 download)

DOWNLOAD NOW!

Book Synopsis Financial Signal Processing and Machine Learning by : Ali N. Akansu

Download or read book Financial Signal Processing and Machine Learning written by Ali N. Akansu and published by John Wiley & Sons. This book was released on 2016-04-21 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Reproducing Kernel Hilbert Spaces in Probability and Statistics

Author : Alain Berlinet
Publisher : Springer Science & Business Media
ISBN 13 : 1441990968
Total Pages : 369 pages
Book Rating : 4.4/5 (419 download)

DOWNLOAD NOW!

Book Synopsis Reproducing Kernel Hilbert Spaces in Probability and Statistics by : Alain Berlinet

Download or read book Reproducing Kernel Hilbert Spaces in Probability and Statistics written by Alain Berlinet and published by Springer Science & Business Media. This book was released on 2011-06-28 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers theoretical questions including the latest extension of the formalism, and computational issues and focuses on some of the more fruitful and promising applications, including statistical signal processing, nonparametric curve estimation, random measures, limit theorems, learning theory and some applications at the fringe between Statistics and Approximation Theory. It is geared to graduate students in Statistics, Mathematics or Engineering, or to scientists with an equivalent level.

Learning Kernel Classifiers

Author : Ralf Herbrich
Publisher : MIT Press
ISBN 13 : 9780262263047
Total Pages : 402 pages
Book Rating : 4.2/5 (63 download)

DOWNLOAD NOW!

Book Synopsis Learning Kernel Classifiers by : Ralf Herbrich

Download or read book Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2001-12-07 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

Kernel Methods in Computer Vision

Author : Christoph H. Lampert
Publisher : Now Publishers Inc
ISBN 13 : 1601982682
Total Pages : 113 pages
Book Rating : 4.6/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Kernel Methods in Computer Vision by : Christoph H. Lampert

Download or read book Kernel Methods in Computer Vision written by Christoph H. Lampert and published by Now Publishers Inc. This book was released on 2009 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.

Applied Multiway Data Analysis

Author : Pieter M. Kroonenberg
Publisher : John Wiley & Sons
ISBN 13 : 0470237996
Total Pages : 614 pages
Book Rating : 4.4/5 (72 download)

DOWNLOAD NOW!

Book Synopsis Applied Multiway Data Analysis by : Pieter M. Kroonenberg

Download or read book Applied Multiway Data Analysis written by Pieter M. Kroonenberg and published by John Wiley & Sons. This book was released on 2008-02-25 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a preeminent authority—a modern and applied treatment of multiway data analysis This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture, environmental sciences, and chemistry. General introductions to multiway data types, methods, and estimation procedures are provided in addition to detailed explanations and advice for readers who would like to learn more about applying multiway methods. Using carefully laid out examples and engaging applications, the book begins with an introductory chapter that serves as a general overview of multiway analysis, including the types of problems it can address. Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, and transformation, as well as robustness and stability issues. Extensive examples are presented within a unified framework consisting of a five-step structure: objectives; data description and design; model and dimensionality selection; results and their interpretation; and validation. Procedures featured in the book are conducted using 3WayPack, which is software developed by the author, and analyses can also be carried out within the R and MATLAB systems. Several data sets and 3WayPack can be downloaded via the book's related Web site. The author presents the material in a clear, accessible style without unnecessary or complex formalism, assuring a smooth transition from well-known standard two-analysis to multiway analysis for readers from a wide range of backgrounds. An understanding of linear algebra, statistics, and principal component analyses and related techniques is assumed, though the author makes an effort to keep the presentation at a conceptual, rather than mathematical, level wherever possible. Applied Multiway Data Analysis is an excellent supplement for component analysis and statistical multivariate analysis courses at the upper-undergraduate and beginning graduate levels. The book can also serve as a primary reference for statisticians, data analysts, methodologists, applied mathematicians, and social science researchers working in academia or industry. Visit the Related Website: http://three-mode.leidenuniv.nl/, to view data from the book.

The Oxford Handbook of Psychological Situations

Author : John F. Rauthmann
Publisher : Oxford University Press
ISBN 13 : 0190263350
Total Pages : 632 pages
Book Rating : 4.1/5 (92 download)

DOWNLOAD NOW!

Book Synopsis The Oxford Handbook of Psychological Situations by : John F. Rauthmann

Download or read book The Oxford Handbook of Psychological Situations written by John F. Rauthmann and published by Oxford University Press. This book was released on 2020-07-01 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Situations matter. They let people express their personalities and values; provoke motivations, emotions, and behaviors; and are the contexts in which people reason and act. The psychological assessment of situations is a new and rapidly developing area of research, particularly within the fields of personality and social psychology. This volume compiles state-of-the-art knowledge on psychological situations in chapters written by experts in their respective research areas. Bringing together historical reviews, theoretical pieces, methodological descriptions, and empirical applications, this volume is the definitive, go-to source for a psychology of situations.

Machine Learning, Optimization, and Data Science

Author : Giuseppe Nicosia
Publisher : Springer Nature
ISBN 13 : 3030375994
Total Pages : 798 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning, Optimization, and Data Science by : Giuseppe Nicosia

Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2020-01-03 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Proceedings of 2nd International Conference on Communication, Computing and Networking

Author : C. Rama Krishna
Publisher : Springer
ISBN 13 : 9811312176
Total Pages : 1039 pages
Book Rating : 4.8/5 (113 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of 2nd International Conference on Communication, Computing and Networking by : C. Rama Krishna

Download or read book Proceedings of 2nd International Conference on Communication, Computing and Networking written by C. Rama Krishna and published by Springer. This book was released on 2018-09-07 with total page 1039 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides insights from the 2nd International Conference on Communication, Computing and Networking organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India on March 29–30, 2018. The book includes contributions in which researchers, engineers, and academicians as well as industrial professionals from around the globe presented their research findings and development activities in the field of Computing Technologies, Wireless Networks, Information Security, Image Processing and Data Science. The book provides opportunities for the readers to explore the literature, identify gaps in the existing works and propose new ideas for research.

Kernel Mean Embedding of Distributions

Author : Krikamol Muandet
Publisher :
ISBN 13 : 9781680832884
Total Pages : 154 pages
Book Rating : 4.8/5 (328 download)

DOWNLOAD NOW!

Book Synopsis Kernel Mean Embedding of Distributions by : Krikamol Muandet

Download or read book Kernel Mean Embedding of Distributions written by Krikamol Muandet and published by . This book was released on 2017-06-28 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics.

Handbook of Mathematical Methods in Imaging

Author : Otmar Scherzer
Publisher : Springer Science & Business Media
ISBN 13 : 0387929193
Total Pages : 1626 pages
Book Rating : 4.3/5 (879 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Mathematical Methods in Imaging by : Otmar Scherzer

Download or read book Handbook of Mathematical Methods in Imaging written by Otmar Scherzer and published by Springer Science & Business Media. This book was released on 2010-11-23 with total page 1626 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

Modern Multidimensional Scaling

Author : Ingwer Borg
Publisher : Springer Science & Business Media
ISBN 13 : 1475727119
Total Pages : 469 pages
Book Rating : 4.4/5 (757 download)

DOWNLOAD NOW!

Book Synopsis Modern Multidimensional Scaling by : Ingwer Borg

Download or read book Modern Multidimensional Scaling written by Ingwer Borg and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multidimensional scaling (MDS) is a technique for the analysis of similarity or dissimilarity data on a set of objects. Such data may be intercorrelations of test items, ratings of similarity on political candidates, or trade indices for a set of countries. MDS attempts to model such data as distances among points in a geometric space. The main reason for doing this is that one wants a graphical display of the structure of the data, one that is much easier to understand than an array of numbers and, moreover, one that displays the essential information in the data, smoothing out noise. There are numerous varieties of MDS. Some facets for distinguishing among them are the particular type of geometry into which one wants to map the data, the mapping function, the algorithms used to find an optimal data representation, the treatment of statistical error in the models, or the possibility to represent not just one but several similarity matrices at the same time. Other facets relate to the different purposes for which MDS has been used, to various ways of looking at or "interpreting" an MDS representation, or to differences in the data required for the particular models. In this book, we give a fairly comprehensive presentation of MDS. For the reader with applied interests only, the first six chapters of Part I should be sufficient. They explain the basic notions of ordinary MDS, with an emphasis on how MDS can be helpful in answering substantive questions.

Nonparametric Methods

Author : P. R. Krishnaiah
Publisher : Elsevier Health Sciences
ISBN 13 :
Total Pages : 1016 pages
Book Rating : 4.:/5 (319 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Methods by : P. R. Krishnaiah

Download or read book Nonparametric Methods written by P. R. Krishnaiah and published by Elsevier Health Sciences. This book was released on 1984 with total page 1016 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classical developments. Linear models. Order statistics and empitical distribution. Estimation procedures. Stochastic aproximation and density estimation. Life testing and reliability. Miscellaneous topics. Applications. Tables.

Asymptotic Statistics

Author : A. W. van der Vaart
Publisher : Cambridge University Press
ISBN 13 : 9780521784504
Total Pages : 470 pages
Book Rating : 4.7/5 (845 download)

DOWNLOAD NOW!

Book Synopsis Asymptotic Statistics by : A. W. van der Vaart

Download or read book Asymptotic Statistics written by A. W. van der Vaart and published by Cambridge University Press. This book was released on 2000-06-19 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master s level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.