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Latent Factor Models for Relational Arrays and Network Data

Latent Factor Models for Relational Arrays and Network Data

This video was recorded at 24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2010. Network and relational data structures have increasingly played a role in the understanding of complex biological, social and other relational systems. Statistical models of such systems can give descriptions of global relational features, characterize local network structure, and provide predictions for missing or future relational data. Latent variable models are a popular tool for describing network and relational patterns. Many of these models are based on well-known matrix decomposition methods, and thus have a rich mathematical framework upon which to build. Additionally, the parameters in these models are easy to interpret: Roughly speaking, a latent variable model... Show More


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