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Open Problems in Spectral Dimensionality Reduction

SBCS: OPEN PRBLMS IN SPECTRAL DIMENSIONALITY REDUCTION

Information Technology

Computing & Information Technology

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Binding Format

Marketing Imprint Name

B2B GT

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B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
1: Introduction.
2: Spectral Dimensionality Reduction.
3: Modelling the Manifold.
4: Intrinsic Dimensionality.
5: Incorporating New Points.
6: Large Scale Data.
7: Postscript.
Index.

Open Problems in Spectral Dimensionality Reduction.css-1p8rvzy{position:absolute;padding-left:8px;}

Open Problems in Spectral Dimensionality Reduction