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Random Subspace Analysis on Canonical Correlation of High Dimensional Data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

High dimensional, low sample, data have singular sample covariance matrices,rendering them impossible to analyse by regular canonical correlation (CC). Byusing random subspace method (RSM) calculation of canonical correlation be-comes possible, and a Monte Carlo analysis shows resulting maximal CC canreliably distinguish between data with true correlation (above 0.5) and with-out. Statistics gathered from RSMCCA can be used to model true populationcorrelation by beta regression, given certain characteristic of data set. RSM-CCA applied on real biological data however show that the method can besensitive to deviation from normality and high degrees of multi-collinearity.

Place, publisher, year, edition, pages
2016. , 38 p.
Keyword [en]
Canonical correlation, Random subspace analysis, high-dimensional statistics
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-295412OAI: oai:DiVA.org:uu-295412DiVA: diva2:933603
Subject / course
Statistics
Supervisors
Examiners
Available from: 2016-06-27 Created: 2016-06-06 Last updated: 2016-06-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
  • de-DE
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  • fi-FI
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  • nn-NB
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Output format
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