Multiple Subject Learning for Inter-Subject Prediction

Sylvain Takerkart (1, 2) , Liva Ralaivola (3)

1 INT - Institut de Neurosciences de la Timone
2 LIF - Laboratoire d'informatique Fondamentale de Marseille
3 QARMA - éQuipe AppRentissage et MultimediA [Marseille]

Multi-voxel pattern analysis has become an important tool for neuroimaging data analysis by allowing to predict a behavioral variable from the imaging patterns. However, standard models do not take into account the differences that can exist between subjects, so that they perform poorly in the inter-subject prediction task. We here introduce a model called Multiple Subject Learning (MSL) that is designed to effectively combine the information provided by fMRI data from several subjects; in a first stage, a weighting of single-subject kernels is learnt using multiple kernel learning to produce a classifier; then, a data shuffling procedure allows to build ensembles of such classifiers, which are then combined by a majority vote. We show that MSL outperforms other models in the inter-subject prediction task and we discuss the empirical behavior of this new model.

Mots clés: classification; MVPA; inter-subject; multi-subject; variability; SVM; MKL; fMRI

Domaines: Neurosciences [q-bio.NC]; Apprentissage [cs.LG]

Sylvain Takerkart, Liva Ralaivola. Multiple Subject Learning for Inter-Subject Prediction. 4TH International Workshop on Pattern Recognition in Neuroimaging (PRNI), Jun 2014, Tübingen, Germany. pp 9-12. ⟨hal-01001987⟩