Online Program Home
My Program

Abstract Details

Activity Number: 127 - SPEED: Statistical Learning and Data Science Speed Session 1, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304110
Title: Sparse Functional Principal Component Analysis in High Dimensions
Author(s): Xiaoyu Hu* and Fang Yao
Companies: peking university and peking university
Keywords: Dimension reduction; Objective-driven tuning; Principal components; Sparsity regimes; Subset selection; Trajectory recovery

With technological advances, more functional data with high dimensionality are available in various fields such as neuroimaging analysis. Due to infinite dimensionality, functional principal component analysis is an important tool for dimension reduction, which however is scarcely researched in high dimensions. We propose sparse principal component analysis for high dimensional functional data based on the relationship between orthonormal basis expansions and multivariate K-L representations. Two sparsity regimes of interest are investigated with theoretical guarantees for the resulting estimators. Simulation and real data examples are provided to lend empirical support to the proposed method, which also performs well in subsequent analysis such as classification.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program