Distributed and Integrated Learning Algorithm for Probabilistic Latent Variable Model

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Probabilistic latent variable models have attracted attention in many scientific fields because of their power and flexibility to model real world phenomena.Latent variable reveal the the underlying structure in data. For example, a probabilistic latent variable model for network such as social network enables researchers to analyze latent community in a network. However, learning probabilistic latent variable model is difficult. Typically, learning probabilistic latent variable model is formulated by an optimization problem which has many poor local solutions. We provided an efficient two learning algorithms to find better local solutions. One is based on a collapsed variational Bayes inference, which is a deterministic algorithm. Another is based on a stochastic search with quantum annealing, which is a stochastic algorithm. We found that these algorithms outperformed existing methods in an academic paper analysis analysis and a network data anaysis.