Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers
This work addresses classification challenges in noisy multi-view data like brain imaging, but it is incremental as it builds on existing Bayesian multi-view learning models.
The authors tackled classification of weak multi-view signals, such as brain imaging data with low signal-to-noise ratio, by proposing a mixture of Bayesian group factor analyzers with shared factors, which increased classification accuracy considerably.
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution. We show that sharing information across the clusters, by sharing factors, increases the classification accuracy considerably; the shared factors essentially form a flexible noise model that explains away the part of data not related to classification. Motivation for the setting comes from single-trial functional brain imaging data, having a very low signal-to-noise ratio and a natural multi-view setting, with the different sensors, measurement modalities (EEG, MEG, fMRI) and possible auxiliary information as views. We demonstrate our model on a MEG dataset.