Learning from Dyadic Data
Abstract
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.
Cite
Text
Hofmann et al. "Learning from Dyadic Data." Neural Information Processing Systems, 1998.Markdown
[Hofmann et al. "Learning from Dyadic Data." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/hofmann1998neurips-learning/)BibTeX
@inproceedings{hofmann1998neurips-learning,
title = {{Learning from Dyadic Data}},
author = {Hofmann, Thomas and Puzicha, Jan and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {466-472},
url = {https://mlanthology.org/neurips/1998/hofmann1998neurips-learning/}
}