Joint topic-document modeling via low-dimensional sparse models

Topic modeling is a well-known approach for document analysis. In this paper, we propose a new model, and corresponding optimization algorithm for topic modeling. Experimental results on polarity classification demonstrate that the new model provides a more accurate characterization for document corpus, and archived higher classification accuracy compared to Latent Dirichlet Allocation (LDA).