Principal Component Neural Networks

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Principal Component Neural Networks

K. I. Diamantaras,S. Y. Kung
Principal Component Neural Networks Book PDF

Author : K. I. Diamantaras,S. Y. Kung
Publisher : Wiley-Interscience
Page : 282 pages
File Size : 46,7 Mb
Release : 1996-03-08
Category : Computers
ISBN : UOM:39015037330696

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Principal Component Neural Networks by K. I. Diamantaras,S. Y. Kung Pdf

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.

Principal Component Analysis

I.T. Jolliffe
Principal Component Analysis Book PDF

Author : I.T. Jolliffe
Publisher : Springer Science & Business Media
Page : 283 pages
File Size : 47,6 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475719048

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Principal Component Analysis by I.T. Jolliffe Pdf

Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

Principal Component Analysis Networks and Algorithms

Xiangyu Kong,Changhua Hu,Zhansheng Duan
Principal Component Analysis Networks and Algorithms Book PDF

Author : Xiangyu Kong,Changhua Hu,Zhansheng Duan
Publisher : Springer
Page : 323 pages
File Size : 53,9 Mb
Release : 2017-01-09
Category : Technology & Engineering
ISBN : 9789811029158

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Principal Component Analysis Networks and Algorithms by Xiangyu Kong,Changhua Hu,Zhansheng Duan Pdf

This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

Principal Manifolds for Data Visualization and Dimension Reduction

Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev
Principal Manifolds for Data Visualization and Dimension Reduction Book PDF

Author : Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev
Publisher : Springer Science & Business Media
Page : 361 pages
File Size : 42,8 Mb
Release : 2007-09-11
Category : Technology & Engineering
ISBN : 9783540737506

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Principal Manifolds for Data Visualization and Dimension Reduction by Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev Pdf

The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.

Artificial Neural Networks-Icann '97

Wulfram Gerstner
Artificial Neural Networks Icann 97 Book PDF

Author : Wulfram Gerstner
Publisher : Springer Science & Business Media
Page : 1300 pages
File Size : 47,6 Mb
Release : 1997-09-29
Category : Computers
ISBN : 3540636315

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Artificial Neural Networks-Icann '97 by Wulfram Gerstner Pdf

Content Description #Includes bibliographical references and index.

An Information-Theoretic Approach to Neural Computing

Gustavo Deco,Dragan Obradovic
An Information Theoretic Approach to Neural Computing Book PDF

Author : Gustavo Deco,Dragan Obradovic
Publisher : Springer Science & Business Media
Page : 265 pages
File Size : 42,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461240167

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An Information-Theoretic Approach to Neural Computing by Gustavo Deco,Dragan Obradovic Pdf

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Artificial Neural Networks and Machine Learning -- ICANN 2013

Valeri Mladenov,Petia Koprinkova-Hristova,Günther Palm,Alessandro Villa,Bruno Apolloni,Nikola K. Kasabov
Artificial Neural Networks and Machine Learning ICANN 2013 Book PDF

Author : Valeri Mladenov,Petia Koprinkova-Hristova,Günther Palm,Alessandro Villa,Bruno Apolloni,Nikola K. Kasabov
Publisher : Springer
Page : 643 pages
File Size : 48,7 Mb
Release : 2013-09-04
Category : Computers
ISBN : 9783642407284

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Artificial Neural Networks and Machine Learning -- ICANN 2013 by Valeri Mladenov,Petia Koprinkova-Hristova,Günther Palm,Alessandro Villa,Bruno Apolloni,Nikola K. Kasabov Pdf

The book constitutes the proceedings of the 23rd International Conference on Artificial Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.

Self-Organising Neural Networks

Mark Girolami
Self Organising Neural Networks Book PDF

Author : Mark Girolami
Publisher : Springer Science & Business Media
Page : 276 pages
File Size : 48,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447108252

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Self-Organising Neural Networks by Mark Girolami Pdf

The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.

Mining Intelligence and Knowledge Exploration

Rajendra Prasath,T. Kathirvalavakumar
Mining Intelligence and Knowledge Exploration Book PDF

Author : Rajendra Prasath,T. Kathirvalavakumar
Publisher : Springer
Page : 845 pages
File Size : 50,5 Mb
Release : 2013-12-16
Category : Computers
ISBN : 9783319038445

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Mining Intelligence and Knowledge Exploration by Rajendra Prasath,T. Kathirvalavakumar Pdf

This book constitutes the proceedings of the First International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2013, held in Tamil Nadu, India on December 2013. The 82 papers presented were carefully reviewed and selected from 334 submissions. The papers cover the topics such as feature selection, classification, clustering, image processing, network security, speech processing, machine learning, information retrieval, recommender systems, natural language processing, language, cognition and computation and other certain problems in dynamical systems.

Statistics and Neural Networks

Jim W. Kay,D. M. Titterington
Statistics and Neural Networks Book PDF

Author : Jim W. Kay,D. M. Titterington
Publisher : Oxford University Press, USA
Page : 290 pages
File Size : 51,9 Mb
Release : 1999
Category : Computers
ISBN : 0198524226

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Statistics and Neural Networks by Jim W. Kay,D. M. Titterington Pdf

Providing a broad overview of important current developments in the area of neural networks, this book highlights likely future trends.

Applications and Innovations in Intelligent Systems XIII

Ann Macintosh,Richard Ellis,Tony Allen
Applications and Innovations in Intelligent Systems XIII Book PDF

Author : Ann Macintosh,Richard Ellis,Tony Allen
Publisher : Springer Science & Business Media
Page : 223 pages
File Size : 44,9 Mb
Release : 2007-10-27
Category : Computers
ISBN : 9781846282249

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Applications and Innovations in Intelligent Systems XIII by Ann Macintosh,Richard Ellis,Tony Allen Pdf

The papers in this volume are the refereed application papers presented at AI-2005, the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2005. The papers present new and innovative developments in the field, divided into sections on Synthesis and Prediction, Scheduling and Search, Diagnosis and Monitoring, Classification and Design, and Analysis and Evaluation. This is the thirteenth volume in the Applications and Innovations series. The series serves as a key reference on the use of AI Technology to enable organisations to solve complex problems and gain significant business benefits. The Technical Stream papers are published as a companion volume under the title Research and Development in Intelligent Systems XXII.

Hebbian Learning and Negative Feedback Networks

Colin Fyfe
Hebbian Learning and Negative Feedback Networks Book PDF

Author : Colin Fyfe
Publisher : Springer Science & Business Media
Page : 383 pages
File Size : 54,8 Mb
Release : 2007-06-07
Category : Computers
ISBN : 9781846281181

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Hebbian Learning and Negative Feedback Networks by Colin Fyfe Pdf

This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.

Generalized Principal Component Analysis

René Vidal,Yi Ma,Shankar Sastry
Generalized Principal Component Analysis Book PDF

Author : René Vidal,Yi Ma,Shankar Sastry
Publisher : Springer
Page : 566 pages
File Size : 44,6 Mb
Release : 2016-04-11
Category : Science
ISBN : 9780387878119

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Generalized Principal Component Analysis by René Vidal,Yi Ma,Shankar Sastry Pdf

This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.

Neural Computing - An Introduction

R Beale,T Jackson
Neural Computing An Introduction Book PDF

Author : R Beale,T Jackson
Publisher : CRC Press
Page : 260 pages
File Size : 46,8 Mb
Release : 1990-01-01
Category : Mathematics
ISBN : 1420050435

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Neural Computing - An Introduction by R Beale,T Jackson Pdf

Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.

Independent Component Analysis

Aapo Hyvärinen,Juha Karhunen,Erkki Oja
Independent Component Analysis Book PDF

Author : Aapo Hyvärinen,Juha Karhunen,Erkki Oja
Publisher : John Wiley & Sons
Page : 505 pages
File Size : 45,6 Mb
Release : 2004-04-05
Category : Science
ISBN : 9780471464198

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Independent Component Analysis by Aapo Hyvärinen,Juha Karhunen,Erkki Oja Pdf

A comprehensive introduction to ICA for students and practitioners Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more. Independent Component Analysis is divided into four sections that cover: * General mathematical concepts utilized in the book * The basic ICA model and its solution * Various extensions of the basic ICA model * Real-world applications for ICA models Authors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.