Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|Title of host publication||2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Number of pages||6|
|Place of Publication||978-1-4673-1025-3|
|State||Published - 2012|
|Conference||2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Period||23/10/2012 → 26/10/2012|
|Name||Machine Learning for Signal Processing|
|Citations||Web of Science® Times Cited: No match on DOI|
- Relational Modelling, Non-parametrics, Infinite Relational Model, Granger Causality
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