Yasutoshi Ida

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We propose a domain-dependent/independent topic switching model based on <i>Bayesian probabilistic modeling</i> for modeling online product reviews that are accompanied with numerical ratings provided by users. In this model, each word is allocated to a domain-dependent topic or a domain-independent topic, and the distribution of topics in an online review(More)
Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This(More)
Stochastic optimization methods are widely used for training of deep neural networks. However, it is still a challenging research problem to achieve effective training by using stochastic optimization methods. This is due to the difficulties in finding good parameters on a loss function that have many saddle points. In this paper, we propose a stochastic(More)
A continuous-valued infinite relational model is proposed as a solution to the co-clustering problem which arises in matrix data or tensor data calculations. The model is a probabilistic model utilizing the framework of Bayesian Nonparametrics which can estimate the number of components in posterior distributions. The original Infinite Relational Model(More)
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