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arXiv · 1802.02343

Multi-View Bayesian Correlated Component Analysis

Simon Kamronn·Andreas Trier Poulsen·Lars Kai Hansen
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Abstract

Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multi-view data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which we denote Bayesian correlated component analysis, evaluates favourably against three relevant algorithms in simulated data. A well-established benchmark EEG dataset is used to further validate the new model and infer the variability of spatial representations across multiple subjects.

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Simon Kamronn, Andreas Trier Poulsen, Lars Kai Hansen. 2018-02-07. Multi-View Bayesian Correlated Component Analysis. https://doi.org/10.1162/neco_a_00774

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