Regularized Probabilistic Latent Semantic Analysis with Continuous Observations

Abstract

Probabilistic latent semantic analysis (PLSA) has been widely used in the machine learning community. However, the original PLSAs are not capable of modeling real-valued observations and usually have severe problems with over fitting. To address both issues, we propose a novel, regularized Gaussian PLSA (RG-PLSA) model that combines Gaussian PLSAs and hierarchical Gaussian mixture models (HGMM). We evaluate our model on supervised human action recognition tasks, using two publicly available datasets. Average classification accuracies of 97.69% and 93.72% are achieved on the Weizmann and KTH Action Datasets, respectively, which demonstrate that the RG-PLSA model outperforms Gaussian PLSAs and HGMMs, and is comparable to the state of the art.