Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
Thomas Hofmann, Jan Puzicha, Michael I. Jordan
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 ap(cid:173) plication ranging from computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework of learn(cid:173) ing from dyadic data by statistical mixture models. Our approach covers different models with fiat and hierarchical latent class struc(cid:173) tures. We propose an annealed version of the standard EM algo(cid:173) rithm for model fitting which is empirically evaluated on a variety of data sets from different domains.