CMX Special Seminar

Quantifying dynamics of a nonlinear and complex system, like our physiological system, from multivariate time series recorded from multimodal sensors is challenging. We develop a new diffusion geometry based sensor fusion algorithm, called alternating diffusion map, to capture the "common" nonlinear information. In addition to its theoretical/statistical properties under the Riemannian manifold model and its relationship with the traditional canonical correlation analysis, its application to sleep dynamics study will be discussed. We show that this unsupervised approach not only leads to a compatible automatic annotation results with the state-of-the-art approach based on deep neural network, but also provides interpretable sleep dynamics features for the ongoing sleep boosting study. If time permits, how to extract spectral features by a nonlinear-type time-frequency analysis will be discussed.