Poster
Nonparametric Latent Feature Models for Link Prediction
Kurt T Miller · Tom Griffiths · Michael Jordan

Mon Dec 7th 07:00 -- 11:59 PM @ None #None

As the availability and importance of relational data -- such as the friendships summarized on a social networking website -- increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on latent class models, adapting nonparametric Bayesian methods to jointly infer how many latent classes there are while learning which entities belong to each class. We pursue a similar approach with a richer kind of latent variable -- latent features -- using a nonparametric Bayesian technique to simultaneously infer the number of features at the same time we learn which entities have each feature. The greater expressiveness of this approach allows us to improve link prediction on three datasets.

Author Information

Kurt T Miller (PDT Partners)
Tom Griffiths (Princeton)
Michael Jordan (UC Berkeley)

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