Learning from Dyadic Data

Dyadzc data refers to a domain with two finite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises naturally in many application rangingfrom computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework of learning fromdyadic data by statistical mixture models. Our approach covers different models with fiat and hierarchical latent class structures. Wepropose an annealed version of the standard EM algorithm for model fitting which is empirically evaluated on a variety of data sets from different domains. 1 Introduction Over the past decade learning from data has become a highly active field of research distributedover many disciplines like pattern recognition, neural computation, statistics,machine learning, and data mining.