Fast Search for Infinite Latent Feature Models
نویسنده
چکیده
We propose several search based alternatives for inference in the Indian Buffet Process (IBP) based models. We consider the case when we only want a maximum a posteriori (MAP) estimate of the latent feature assignment matrix. If true posterior samples are required, these MAP estimates can also serve as intelligent initializers for MCMC based algorithms. Another advantage of the proposed methods is that they can process one observation at a time making it possible to do inference in an online setting. Experimental evidences suggest that these algorithms can give us computational benefits of an order of magnitude over Gibbs sampling (or its sequential variant the particle filter) traditionally used in IBP based models.
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