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Statistical and Knowledge Supported Visualization of Multivariate data

Fontes, Magnus LU (2012) In Analysis for Science, Engineering and Beyond Springer Proceedings in Mathematics Volume 6. p.143-173
Abstract
In the present work we have selected a collection of statistical and mathematical tools useful for the exploration of multivariate data and we present them in a form that is meant to be particularly accessible to a classically trained mathematician.

We give self contained and streamlined introductions to principal component analysis, multidimensional scaling and statistical hypothesis testing. Within the presented mathematical framework we then propose a general exploratory methodology for the investigation of real world high dimensional datasets that builds on statistical and knowledge supported visualizations.

We exemplify the proposed methodology by applying it to several different genomewide DNA-microarray datasets.... (More)
In the present work we have selected a collection of statistical and mathematical tools useful for the exploration of multivariate data and we present them in a form that is meant to be particularly accessible to a classically trained mathematician.

We give self contained and streamlined introductions to principal component analysis, multidimensional scaling and statistical hypothesis testing. Within the presented mathematical framework we then propose a general exploratory methodology for the investigation of real world high dimensional datasets that builds on statistical and knowledge supported visualizations.

We exemplify the proposed methodology by applying it to several different genomewide DNA-microarray datasets. The exploratory methodology should be seen as an embryo that can be expanded and developed in many directions. As an example we point out some recent promising advances in the theory for random matrices that, if further developed, potentially could provide practically useful and theoretically well founded estimations of information content in dimension reducing visualizations. We hope that the present work can serve as an introduction to, and help to stimulate more research within, the interesting and rapidly expanding field of data exploration. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Multidimensional scaling, Principal component analysis, Multivariate data, Visualization, Multiple hypothesis testing
in
Analysis for Science, Engineering and Beyond
editor
Kalle, Åström; Lars-Erik, Persson; Sergei D., Silvestrov; ; and
volume
Springer Proceedings in Mathematics Volume 6
pages
143 - 173
publisher
Springer
external identifiers
  • scopus:84893553425
ISSN
2190-5614
2190-5622
ISBN
978-3-642-20235-3 (print)
978-3-642-20236-0 (online)
DOI
10.1007/978-3-642-20236-0_6
language
English
LU publication?
yes
id
b9c519b0-6a07-4357-8453-7173b82e6087 (old id 2430811)
date added to LUP
2012-07-10 17:13:30
date last changed
2017-06-04 03:10:07
@inbook{b9c519b0-6a07-4357-8453-7173b82e6087,
  abstract     = {In the present work we have selected a collection of statistical and mathematical tools useful for the exploration of multivariate data and we present them in a form that is meant to be particularly accessible to a classically trained mathematician.<br/><br>
We give self contained and streamlined introductions to principal component analysis, multidimensional scaling and statistical hypothesis testing. Within the presented mathematical framework we then propose a general exploratory methodology for the investigation of real world high dimensional datasets that builds on statistical and knowledge supported visualizations.<br/><br>
We exemplify the proposed methodology by applying it to several different genomewide DNA-microarray datasets. The exploratory methodology should be seen as an embryo that can be expanded and developed in many directions. As an example we point out some recent promising advances in the theory for random matrices that, if further developed, potentially could provide practically useful and theoretically well founded estimations of information content in dimension reducing visualizations. We hope that the present work can serve as an introduction to, and help to stimulate more research within, the interesting and rapidly expanding field of data exploration.},
  author       = {Fontes, Magnus},
  editor       = {Kalle, Åström and Lars-Erik, Persson and Sergei D., Silvestrov},
  isbn         = {978-3-642-20235-3 (print)},
  issn         = {2190-5614},
  keyword      = {Multidimensional scaling,Principal component analysis,Multivariate data,Visualization,Multiple hypothesis testing},
  language     = {eng},
  pages        = {143--173},
  publisher    = {Springer},
  series       = {Analysis for Science, Engineering and Beyond},
  title        = {Statistical and Knowledge Supported Visualization of Multivariate data},
  url          = {http://dx.doi.org/10.1007/978-3-642-20236-0_6},
  volume       = {Springer Proceedings in Mathematics Volume 6},
  year         = {2012},
}