Alternating diffusion for common manifold learning with application to sleep stage assessment

摘要: In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract common source variability from multiple measurements. This is based on alternating-diffusion particularly adapted time series. We show that extracted sensors as if it were only variability, by standard single sensor, without influence sensor-specific variables. addition, application sleep stage assessment. demonstrate that, indeed, through alternating-diffusion, information hidden inside respiratory signals can be better captured compared single-modal methods.