@CONFERENCE\{IMM2012-06508,
    author       = "T. Herlau and M. M{\o}rup and M. N. Schmidt and L. K. Hansen",
    title        = "Modeling dense relational data",
    year         = "2012",
    booktitle    = "Machine Learning for Signal Processing (MLSP), 2012 {IEEE} International Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6508-full.html",
    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."
}