Open Problems in Spectral Dimensionality Reduction

Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Artificial Intelligence, General Computing
Cover of the book Open Problems in Spectral Dimensionality Reduction by Harry Strange, Reyer Zwiggelaar, Springer International Publishing
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Author: Harry Strange, Reyer Zwiggelaar ISBN: 9783319039435
Publisher: Springer International Publishing Publication: January 7, 2014
Imprint: Springer Language: English
Author: Harry Strange, Reyer Zwiggelaar
ISBN: 9783319039435
Publisher: Springer International Publishing
Publication: January 7, 2014
Imprint: Springer
Language: English

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.

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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.

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