Handling incomplete matrix data via continuous-valued infinite relational model

Abstract

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 cannot handle continuous-valued or multi-dimensional data directly. Our proposed model overcomes the data expression restrictions by utilizing the proposed likelihood, which can handle many types of data. The posterior distribution is estimated via variational inference. Using real-world data, we show that the proposed model outperforms the original model in terms of AUC score and efficiency for a movie recommendation task. (111 words).