Feature Selection for Diffusion Methods Within a Supervised Context

Abstract

We apply diffusion geometry to sociopolitical and public health datasets. Our specific goal is to reveal hidden trends and narratives behind UN voting records and alcohol questionnaire response patterns. Importantly, seeking those hidden variables in a supervised context, e.g. alcohol-abuse, can be problematic for diffusion geometry. We suggest two approaches to deal with these shortcomings. First, we develop a correlation-based hierarchical clustering algorithm that exposes sub-patterns in the feature (response) space; this works in the UN voting context. Second, we introduce a feature selection algorithm based on a second-order correlation measure to guide diffusion embeddings; this significantly improves the performance of diffusion methods in the alcohol context. Together they suggest how to structure embeddings when there exist strong correlations among features irrelevant to a given labeling function.