This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that...
In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic mo...
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
We propose a new method to select relevant images to the given keywords from images gathered from the Web based on the Probabilistic Latent Semantic Analysis (PLSA) model which is...
In many topic identification applications, supervised training labels are indirectly related to the semantic content of the documents being classified. For example, many topical...