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

Sami Remes, Tommi Mononen, Samuel Kaski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionProfessional

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
ToimittajatIrina Rish, Leila Wehbe, Georg Langs, Moritz Grosse-Wentrup, Brian Murphy, Guillermo Cecchi
Julkaisupaikkahttps://arxiv.org/abs/1605.04435
Sivumäärä8
TilaJulkaistu - 14 toukokuuta 2016
OKM-julkaisutyyppiD3 Ammatillisen konferenssin julkaisusarja
TapahtumaWorkshop on Machine Learning and Interpretation in Neuroimaging - Montreal, Kanada
Kesto: 11 joulukuuta 201512 joulukuuta 2015
Konferenssinumero: 5
https://sites.google.com/site/mliniworkshop2015/

Workshop

WorkshopWorkshop on Machine Learning and Interpretation in Neuroimaging
LyhennettäMLINI
MaaKanada
KaupunkiMontreal
Ajanjakso11/12/201512/12/2015
www-osoite

Sormenjälki Sukella tutkimusaiheisiin 'Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä