Modeling dense relational data

Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars K. Hansen

AbstractRelational 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.
TypeConference paper [With referee]
ConferenceMachine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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