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Erschienen in:
Statistical Inference Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

6. Dimensionality Reduction

verfasst von : Daniel Durstewitz

Erschienen in: Advanced Data Analysis in Neuroscience

Verlag: Springer International Publishing

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Abstract

For the purpose of visualization and for the ease of interpretation, to remove redundancies from the data or to combat the curse of dimensionality (Sect. 4.​4), it may be useful to reduce the dimensionality of the original p-dimensional feature space. This, of course, should be done in a way that minimizes the potential loss of information, where the precise definition of “loss of information” may depend on the statistical and scientific questions asked. There are both linear and nonlinear methods for dimensionality reduction. This chapter will start with the by far most popular procedure, principal component analysis.

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Vorheriges Kapitel Clustering and Density Estimation
Nächstes Kapitel Linear Time Series Analysis
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Metadaten
Titel
Dimensionality Reduction
verfasst von
Daniel Durstewitz
Copyright-Jahr
2017
Buch
Advanced Data Analysis in Neuroscience

Print ISBN: 978-3-319-59974-8

Electronic ISBN: 978-3-319-59976-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59976-2_6