Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weighted-Based Approach

Abstract

The ability to reliably infer the nature of telephone conversations opens up a variety of applications, ranging from designing context-sensitive user interfaces on smartphones, to providing new tools for social psychologists and social scientists to study and understand social life of different subpopulations within different contexts. Using a unique corpus of everyday telephone conversations collected from eight residences over the duration of a year, we investigate the utility of popular features, extracted solely from the content, in classifying business-oriented calls from others. Through feature selection experiments, we find that the discrimination can be performed robustly for a majority of the calls using a small set of features. Remarkably, features learned from unsupervised methods, specifically latent Dirichlet allocation, perform almost as well as with as those from supervised methods. The unsupervised clusters learned in this task shows promise of finer grain inference of social nature of telephone conversations.