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Open Problems in Spectral Dimensionality Reduction : Computer Science (R0) - Harry Strange

Open Problems in Spectral Dimensionality Reduction

By: Harry Strange, Reyer Zwiggelaar

eText | 7 January 2014

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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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Non-FictionComputing & I.T.Graphical & Digital Media Applications3D Graphics & ModellingComputer Programming & Software DevelopmentAlgorithms & Data StructuresComputer ScienceImage ProcessingDatabasesArtificial IntelligenceComputer Vision

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