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Open Problems in Spectral Dimensionality Reduction

  • Book
  • © 2014

Overview

Authors:
  1. Harry Strange

    1. Department of Computer Science, Aberystwyth University, United Kingdom

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    You can also search for this author in PubMed   Google Scholar

  2. Reyer Zwiggelaar

    1. Department of Computer Science, Aberystwyth University, United Kingdom

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Provides a clear and concise overview of spectral dimensionality reduction
  • Offers uniquely practical knowledge without requiring a background in the area
  • Suggests interesting starting points for future research in this area
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Introduction

    • Harry Strange, Reyer Zwiggelaar
    Pages 1-5

  3. Spectral Dimensionality Reduction

    • Harry Strange, Reyer Zwiggelaar
    Pages 7-22

  4. Modelling the Manifold

    • Harry Strange, Reyer Zwiggelaar
    Pages 23-40

  5. Intrinsic Dimensionality

    • Harry Strange, Reyer Zwiggelaar
    Pages 41-52

  6. Incorporating New Points

    • Harry Strange, Reyer Zwiggelaar
    Pages 53-68

  7. Large Scale Data

    • Harry Strange, Reyer Zwiggelaar
    Pages 69-81

  8. Postscript

    • Harry Strange, Reyer Zwiggelaar
    Pages 83-90

  9. Back Matter

    Pages 91-92
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Computer Science, Aberystwyth University, United Kingdom

    Harry Strange, Reyer Zwiggelaar

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