Open Problems in Spectral Dimensionality Reduction
Series: SpringerBriefs in Computer Science;
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22 184 Ft
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Product details:
- Edition number 2014
- Publisher Springer International Publishing
- Date of Publication 21 January 2014
- Number of Volumes 1 pieces, Book
- ISBN 9783319039428
- Binding Paperback
- No. of pages92 pages
- Size 235x155 mm
- Weight 1708 g
- Language English
- Illustrations IX, 92 p. 20 illus., 15 illus. in color. Illustrations, black & white 0
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Long description:
The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects or measurements whilst retaining important information. Spectral dimensionality reduction is one such tool for the data processing pipeline. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.
MoreTable of Contents:
Introduction.- Spectral Dimensionality Reduction.- Modelling the Manifold.- Intrinsic Dimensionality.- Incorporating New Points.- Large Scale Data.- Postcript.
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