Assigning Features using Additive Clustering

Abstract

If the promise of computational modeling is to be fully realized in higher-level cognitive domains such as language processing, principled methods must be developed to construct the semantic representations that serve as these models’ input and/or output. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically assigning discrete features to objects using only pairwise similarity data. Similar approaches have not been widely adopted in the past, as existing methods for the unsupervised learning of such models do not scale well to large problems. We propose a new algorithm for additive clustering, based on heuristic combinatorial optimization. Through extensive empirical tests on both human and synthetic data, we find that the new algorithm is more effective than previous methods and that it also scales well to larger problems. By making additive clustering practical, we take a significant step toward scaling connectionist models beyond hand-coded examples.