Discovery of activity composites using topic models: An analysis of unsupervised methods

Abstract In this work we investigate unsupervised activity discovery approaches using three topic model (TM) approaches, based on Latent Dirichlet Allocation (LDA), n -gram TM (NTM), and correlated TM (CTM). While \{LDA\} structures activity primitives, \{NTM\} adds primitive sequence information, and \{CTM\} exploits co-occurring topics. We use an activity composite/primitive abstraction and analyze three public datasets with different properties that affect the discovery, including primitive rate, activity composite specificity, primitive sequence similarity, and composite-instance ratio. We compare the activity composite discovery performance among the \{TM\} approaches and against a baseline using k -means clustering. We provide guidelines for method and optimal \{TM\} parameter selection, depending on data properties and activity primitive noise. Results indicate that \{TMs\} can outperform k -means clustering up to 17%, when composite specificity is low. LDA-based \{TMs\} showed higher robustness against noise compared to other \{TMs\} and k -means.