Modeling dense relational data |
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| Abstract | 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. |
| Type | Conference paper [With referee] |
| Conference | Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on |
| Year | 2012 |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |