Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

Motivated by analyzing long-term physiological time series, we design a robust and scalable spectral embedding algorithm, coined the algorithm RObust and Scalable Embedding via LANdmark Diffusion (ROSE-LAND). The key is designing a diffusion process on the dataset, where the diffusion is forced to interchange on a small subset called the landmark set. In addition to demonstrating its application to spectral clustering and image segmentation, the algorithm is applied to study the long-term arterial blood pressure waveform dynamics during a liver transplant operation lasting for 12 hours long.