Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering

• Activity Number: 121 - Emerging Statistical Methods for Structured and Multimodal Data Analysis
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Yang Ning*
• Companies: Cornell University
• Keywords: high dimensional estimation

Abstract

This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse. In order to identify the loading matrix A, we require the existence of pure variables, which are components of X that are associated, via A, with one and only one latent factor.