Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns - Hardcover

Kirby, Michael

 
9780471239291: Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
Hardcover
ISBN 10: 0471239291 ISBN 13: 9780471239291
Verlag: Wiley-Interscience, 2001

Inhaltsangabe

This book addresses the most efficient methods of pattern analysis using wavelet decomposition. Readers will learn to analyze data in order to emphasize the differences between closely related patterns and then categorize them in a way that is useful to system users.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

MICHAEL KIRBY is a professor in the Department of Mathematics at Colorado State University in Fort Collins, Colorado. He has worked in the field of data reduction for well over a decade.

Von der hinteren Coverseite

An analysis of large data sets from an empirical and geometric viewpoint

Data reduction is a rapidly emerging field with broad applications in essentially all fields where large data sets are collected and analyzed. Geometric Data Analysis is the first textbook to focus on the geometric approach to this problem of developing and distinguishing subspace and submanifold techniques for low-dimensional data representation. Understanding the geometrical nature of the data under investigation is presented as the key to identifying a proper reduction technique.

Focusing on the construction of dimensionality-reducing mappings to reveal important geometrical structure in the data, the sequence of chapters is carefully constructed to guide the reader from the beginnings of the subject to areas of current research activity. A detailed, and essentially self-contained, presentation of the mathematical prerequisites is included to aid readers from a broad variety of backgrounds. Other topics discussed in Geometric Data Analysis include:
* The Karhunen-Loeve procedure for scalar and vector fields with extensions to missing data, noisy data, and data with symmetry
* Nonlinear methods including radial basis functions (RBFs) and backpropa-gation neural networks
* Wavelets and Fourier analysis as analytical methods for data reduction
* Expansive discussion of recent research including the Whitney reduction network and adaptive bases codeveloped by the author
* And much more

The methods are developed within the context of many real-world applications involving massive data sets, including those generated by digital imaging systems and computer simulations of physical phenomena. Empirically based representations are shown to facilitate their investigation and yield insights that would otherwise elude conventional analytical tools.

Aus dem Klappentext

An analysis of large data sets from an empirical and geometric viewpoint

Data reduction is a rapidly emerging field with broad applications in essentially all fields where large data sets are collected and analyzed. Geometric Data Analysis is the first textbook to focus on the geometric approach to this problem of developing and distinguishing subspace and submanifold techniques for low-dimensional data representation. Understanding the geometrical nature of the data under investigation is presented as the key to identifying a proper reduction technique.

Focusing on the construction of dimensionality-reducing mappings to reveal important geometrical structure in the data, the sequence of chapters is carefully constructed to guide the reader from the beginnings of the subject to areas of current research activity. A detailed, and essentially self-contained, presentation of the mathematical prerequisites is included to aid readers from a broad variety of backgrounds. Other topics discussed in Geometric Data Analysis include:
* The Karhunen-Loeve procedure for scalar and vector fields with extensions to missing data, noisy data, and data with symmetry
* Nonlinear methods including radial basis functions (RBFs) and backpropa-gation neural networks
* Wavelets and Fourier analysis as analytical methods for data reduction
* Expansive discussion of recent research including the Whitney reduction network and adaptive bases codeveloped by the author
* And much more

The methods are developed within the context of many real-world applications involving massive data sets, including those generated by digital imaging systems and computer simulations of physical phenomena. Empirically based representations are shown to facilitate their investigation and yield insights that would otherwise elude conventional analytical tools.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Wiley-Interscience
Erscheinungsdatum
2001
Sprache
Englisch
ISBN 10 
0471239291
ISBN 13 
9780471239291
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
384
Kontakt zum Hersteller
Wiley-VCH GmbH
product_safety@wiley.com

Boschstrasse 12
Weinheim
69469
Deutschland