Learning a Hierarchical Latent Semantic Model for Multimedia Data

This paper develops a hierarchical feature representation that is based on a Bayesian non-parametric method. Feature learning is an important issue in classification and data analysis. It can improve the classification performance and increase the convenience of data processing and analysis. Popular methods of representation learning include methods that are based on mixture models or dictionary learning methods. However, current methods have some disadvantages. The use of a traditional mixture model, such as the Gaussian mixture model (GMM), involves the model selection problem and suffers a lack of hierarchy between components. Inspired by h-LDA, distance-based Gaussian hierarchical Dirichlet allocation (distance-based GhLDA) is proposed herein. This method can automatically determine the number of components and construct a hierarchical representation. The distance function between data is used in the prior distribution. The learnt representation in the proposed model has the advantage of hLDA, which can handle shared components and distinct components. The quantization loss problem, which commonly arises when a topic model is used to deal with continuous data, can be solved by assuming that the distribution of words follows a Gaussian rather than a Dirichlet distribution. The performance of the proposed model in solving audio and image classification problems is evaluated. Experimental results indicate that the distance-based GhLDA outperforms baseline methods.