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 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.

Authors

• Thomas Hofmann ETH Zürich
• Jan Puzicha
• Michael Jordan

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