@inproceedings{meeds2006modeling,
author = {Meeds, Ted and Ghahramani, Zoubin and Neal, Radford and Roweis, Sam},
title = {Modeling Dyadic Data with Binary Latent Factors},
booktitle = {Neural Information Processing Systems},
year = {2006},
month = {December},
abstract = {We introduce binary matrix factorization, a novel model for unsupervised matrix decomposition. The decomposition is learned by fitting a non-parametric Bayesian probabilistic model with binary latent variables to a matrix of dyadic data. Unlike bi-clustering models, which assign each row or column to a single cluster based on a categorical hidden feature, our binary feature model reflects the prior belief that items and attributes can be associated with more than one latent cluster at a time. We provide simple learning and inference rules for this new model and show how to extend it to an infinite model in which the number of features is not a priori fixed but is allowed to grow with the size of the data.},
url = {http://approjects.co.za/?big=en-us/research/publication/modeling-dyadic-data-binary-latent-factors/},
edition = {Neural Information Processing Systems},
}