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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

5. Data-Dimension Reductions: A Comparison

verfasst von : Peter Sarlin

Erschienen in: Mapping Financial Stability

Verlag: Springer Berlin Heidelberg

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Abstract

Data and dimension reduction techniques, and particularly their combination for Data-Dimension Reductions (DDR), have in many fields and tasks held promise for representing data in an easily understandable format. However, comparing methods and finding the most suitable one is a challenging task. In the previous chapter, we discussed the aim of dimension reduction in terms of three tasks. This chapter compares DDR combinations to financial performance analysis. To this end, after a general review of the literature on comparisons of data and dimension reduction methods, we discuss the aims and needs of DDR combinations in general and for the task at hand in particular.

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Vorheriges Kapitel Data and Dimension Reduction
Nächstes Kapitel Extending the SOM
Fußnoten
1
While Relative MDS (Naud and Duch 2000) allows to add new data to the basis of an old MDS, it does still not update all distances within the mapping.
 
2
When training SOMs, one has to set a number of free parameters. A set of quality measures is used to track the topographic and quantization accuracy as well as clustering of the map. Given the purpose herein, details about the parametrization of the models in the experiments are not presented in depth.
 
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Metadaten
Titel
Data-Dimension Reductions: A Comparison
verfasst von
Peter Sarlin
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Mapping Financial Stability

Print ISBN: 978-3-642-54955-7

Electronic ISBN: 978-3-642-54956-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-54956-4_5