Fast brain decoding with random sampling and random projections

Andrés Hoyos-Idrobo (1, 2) , Gaël Varoquaux (2) , Bertrand Thirion (2)

1 NEUROSPIN - Service NEUROSPIN
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

Machine learning from brain images is a central tool for image-based diagnosis and diseases characterization. Predicting behavior from functional imaging, brain decoding, analyzes brain activity in terms of the behavior that it implies. While these multivariate techniques are becoming standard brain mapping tools, like mass-univariate analysis, they entail much larger computational costs. In an time of growing data sizes, with larger cohorts and higher-resolutions imaging, this cost is increasingly a burden. Here we consider the use of random sampling and projections as fast data approximation techniques for brain images. We evaluate their prediction accuracy and computation time on various datasets and discrimination tasks. We show that the weight maps obtained after random sampling are highly consistent with those obtained with the whole feature space, while having a fair prediction performance. Altogether, we present the practical advantage of random sampling methods in neuroimaging, showing a simple way to embed back the reduced coefficients, with only a small loss of information.

Mots clés: Index Terms—Nyström; High-dimensional estimators; machine learning; brain imaging

Domaines: Machine Learning [stat.ML]; Imagerie médicale

Andrés Hoyos-Idrobo, Gaël Varoquaux, Bertrand Thirion. Fast brain decoding with random sampling and random projections. PRNI 2016: the 6th International Workshop on Pattern Recognition in Neuroimaging, Jun 2016, Trento, Italy. ⟨hal-01313814⟩