Distributed and Integrated Learning Algorithm for Probabilistic Latent Variable Model

概率潜变量模型由于其模拟现实世界现象的能力和灵活性而受到许多科学领域的关注。潜变量揭示了数据的底层结构。例如，社会网络等网络的概率潜变量模型使研究人员能够分析网络中的潜在社区。然而，学习概率潜变量模型是一个困难的问题。典型的学习概率潜变量模型是由一个具有许多不良局部解的优化问题来表述的。我们提供了一种高效的两种学习算法来寻找更好的局部解。一种是基于崩溃变分贝叶斯推理，这是一种确定性算法。另一种是基于量子退火的随机搜索，这是一种随机算法。我们发现这些算法在学术论文分析和网络数据分析中优于现有方法。

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.