A1262
Title: Filtrated common functional principal component analysis of multi-group functional data
Authors: Shuhao Jiao - City University of Hong Kong (Hong Kong) [presenting]
Ron Frostig - University of California Irvine (United States)
Hernando Ombao - KAUST (Saudi Arabia)
Abstract: Local field potentials (LFPs) are signals that measure electrical activities in localized cortical regions and are collected from multiple tetrodes implanted across a patch on the surface of the cortex. In many cases, multi-tetrode LFP trajectories contain both global variation patterns and idiosyncratic variation patterns, and such structure is very informative to the data mechanism. Therefore, one goal is to develop an efficient algorithm that is able to capture and quantify both global and idiosyncratic features. The novel filtrated common functional principal components (filt-fPCA) method is developed, a novel forest-structured fPCA for multi-group functional data. A major advantage of the proposed filt-fPCA method is its ability to extract the common components in a flexible multi-resolution manner. The proposed approach is highly data-driven, and no prior knowledge of ground-truth data structure is needed, making it suitable for analyzing complex multi-group functional data. In addition, the filt-fPCA method is able to produce parsimonious, interpretable, and efficient functional reconstruction (low reconstruction error) for multi-group functional data with orthonormal basis functions. The proposed filt-fPCA method is employed to study the impact of a shock (induced stroke) on the synchrony structure of rats' brains.
