Open Problems in Spectral Dimensionality Reduction

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.

List of contents

Introduction.- Spectral Dimensionality Reduction.- Modelling the Manifold.- Intrinsic Dimensionality.- Incorporating New Points.- Large Scale Data.- Postcript.

Product details

• Authors: Harry Strange, Reyer Zwiggelaar
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 21.01.2014
• EAN: 9783319039428
• ISBN: 978-3-31-903942-8
• No. of pages: 92
• Dimensions: 162 mm x 230 mm x 9 mm
• Weight: 172 g
• Illustrations: IX, 92 p. 20 illus., 15 illus. in color.
• Series: SpringerBriefs in Computer Science
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; C, Algorithms, Artificial Intelligence, computer science, Computer Vision, Theory of Computation, Image Processing and Computer Vision, Data Structures and Information Theory, Algorithms & data structures, Optical data processing, Algorithm Analysis and Problem Complexity, Image processing, Data structures (Computer science), Data Structures