Modelling dense relational data

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Relational modelling classically consider sparse and discrete data. Measures of influence computed pairwise between temporal sources naturally give rise to dense continuous-valued matrices, for instance p-values from Granger causality. Due to asymmetry or lack of positive definiteness they are not naturally suited for kernel K-means. We propose a generative Bayesian model for dense matrices which generalize kernel K-means to consider off-diagonal interactions in matrices of interactions, and demonstrate its ability to detect structure on both artificial data and two real data sets.
Original languageEnglish
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
Place of publication978-1-4673-1025-3
PublisherIEEE
Publication date2012
ISBN (print)978-1-4673-1024-6
DOIs
StatePublished

Conference

Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountrySpain
CitySantander
Period23/10/1226/10/12
Internet addresshttp://mlsp2012.conwiz.dk/
NameMachine Learning for Signal Processing
ISSN (Print)1551-2541
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Relational Modelling, Non-parametrics, Infinite Relational Model, Granger Causality
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