Search

Workshop

Higher Order Partial Least Squares and an Application to Neuroscience

  • Luke Oeding (Auburn University)
E1 05 (Leibniz-Saal)

Abstract

Partial least squares (PLS) is a method to discover a functional dependence between two sets of variables X and Y. PLS attempts to maximize the covariance between X and Y by projecting both onto new subspaces. Higher order partial least squares (HOPLS) comes into play when the sets of variables have additional tensorial structure. Simultaneous optimization of subspace projections may be obtained by a multilinear singular value decomposition (MSVD). I'll review PLS and SVD, and explain their higher order counterparts. Finally I'll describe recent work with G. Deshpande, A. Cichocki, D. Rangaprakash, and X.P. Hu where we propose to use HOPLS and Tensor Decompositions to discover latent linkages between EEG and fMRI signals from the brain, and ultimately use this to drive Brain Computer Interfaces (BCI)'s with the low size, weight and power of EEG, but with the accuracy of fMRI.

Links

Saskia Gutzschebauch

Max-Planck-Institut für Mathematik in den Naturwissenschaften Contact via Mail

Christiane Görgen

Max-Planck-Institut für Mathematik in den Naturwissenschaften

Sara Kališnik Verovšek

Max-Planck-Institut für Mathematik in den Naturwissenschaften

Vlada Limic

Université de Strasbourg and CNRS, Paris