|
Abstract:
|
Many applications generate spatial-temporal data that exhibit lower-rank smooth movements mixed with higher-rank noises. Separating the signal from the noise is important for us to visualize and understand the lower-rank movements. It is also often the case that the lower rank dynamics have multiple independent components that correspond to different trends or functionality of the system under study. In this presentation, we present a novel filtering method for identifying lower-rank dynamics and its components embedded in a high dimensional spatial-temporal system, with applications to climate data.
|
