Popular methods for probabilistic topic modeling like the Latent Dirichlet Allocation (LDA) and Correlated Topic Models (CTM) share an important property, i.e., using a common set of topics to model all the data. This property can be too restrictive for modeling complex data entries where multiple fields of heterogeneous data jointly provide rich information about each object or event. We propose a new extension of the CTM method to enable modeling with multi-field topics in a global graphical structure, and a mean-field variational algorithm to allow joint learning of multinomial topic models from discrete data and Gaussian-style topic models for real-valued data. We conducted experiments with both simulated and real data, and observed that the multi-field CTM outperforms a conventional CTM in both likelihood maximization and perplexity reduction. A deeper analysis on the simulated data reveals that the superior performance is the result of successful discovery of the mapping among field-specific topics and observed data.
展开▼
