Learning from Dyadic Data

Dyadic data refers to a domain with two nite 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 ranging from computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework of learning from dyadic data by statistical mixture models. Our approach covers di erent models with at and hierarchical latent class structures. We propose an annealed version of the standard EM algorithm for model tting which is empirically evaluated on a variety of data sets from di erent domains.

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Thomas Hofmann, Jan Puzicha, Michael I. Jordan
• Keywords: Di Erent Models, Dyadic Data, NIPS 1998, NIPS 2007, Statistical Mixture Models