@inproceedings{
    Savia09icassp,
    author = "Savia, Eerika and Klami, Arto and Kaski, Samuel",
    corerank = "B",
    booktitle = "Proceedings of ICASSP 09, the International Conference on Acoustics, Speech, and Signal Processing",
    title = "Fast Dependent Components for f{MRI} Analysis",
    abstract = "Canonical correlation analysis (CCA) can be used to find correlating projections of two datasets with co-occurring samples. Instead of correlation, we would typically want to find more general dependencies, measured by mutual information. Variants of CCA based on non-parametric estimation of mutual information have been proposed previously; they outperform traditional CCA for non-Gaussian data but require infeasible amounts of computation for already quite modest sample sizes. We introduce a novel variant that uses a semiparametric estimate leading to a considerably faster algorithm. We apply the method on searching for statistical dependencies between multi-sensory stimuli and functional magnetic resonance imaging (fMRI) of brain activity -- in contrast to using regression on either of them.",
    flags = "copy AIRC HIIT",
    year = "2009",
    impactfactor = "D3",
    pages = "1737--1740"
}