High-dimensional Data Analysis: From Optimal Metrics to Feature Selection - Softcover

François, Damien

Softcover

ISBN 10: 3836493098 ISBN 13: 9783836493093

Publisher: VDM Verlag, 2008

Synopsis

High-dimensional data are everywhere: texts, sounds, spectra, images, etc. However, many data analysis tools (coming from statistics, artificial intelligence, etc.) were designed for low-dimensional data. Many of the assumptions behind data analysis tools are not transposable to high-dimensional data. For instance, the Euclidean distance concentrates in high-dimensional spaces; all distances seem identical! It furthermore does not distinguish between relenvant and irrelevant features. In Part One of the book, the phenomenon of the concentration of the distances is considered, and its consequences on data analysis tools are studied. Part Two focuses on the problem of feature selection in the case of a large number of initial features. Most of the concepts studied and presented in this thesis are illustrated on chemometric data, and more particularly on spectral data, with the objective of inferring a physical or chemical property of a material by analysis the spectrum of the light it reflects.

About this title

About the Author

Damien François received the M.Sc. degree in Computer Science and Engineering from the Université catholique de Louvain (Belgium) in 2002 and the Ph.D. degree in Applied Mathematics from the same university in 2007. His main research interests lie in feature/model selection, meta learning and neural networks.

Edition details

• Publisher: VDM Verlag
• Publication date: 2008
• Language: English
• ISBN 10: 3836493098
• ISBN 13: 9783836493093
• Binding: Paperback
• Number of pages: 172