Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

Sami Remes, Tommi Mononen, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
EditorsIrina Rish, Leila Wehbe, Georg Langs, Moritz Grosse-Wentrup, Brian Murphy, Guillermo Cecchi
Place of Publicationhttps://arxiv.org/abs/1605.04435
Number of pages8
Publication statusPublished - 14 May 2016
MoE publication typeD3 Professional conference proceedings
EventWorkshop on Machine Learning and Interpretation in Neuroimaging - Montreal, Canada
Duration: 11 Dec 201512 Dec 2015
Conference number: 5
https://sites.google.com/site/mliniworkshop2015/

Workshop

WorkshopWorkshop on Machine Learning and Interpretation in Neuroimaging
Abbreviated titleMLINI
CountryCanada
CityMontreal
Period11/12/201512/12/2015
Internet address

Keywords

  • Bayesian group factor analysis
  • brain decoding
  • MEG
  • multi-view learning
  • variational Bayesian inference

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