document: Discriminative topic modeling based on manifold learning Topic modeling has been popularly used for data analysis in various domains including text documents . Previous topic models , such as probabilistic Latent Semantic Analysis ( pLSA ) and Latent Dirichlet Allocation ( LDA ) , have shown impressive success in discovering low-rank hidden structures for modeling text documents . These models , however , do not take into account the manifold structure of data , which is generally informative for the non-linear dimensionality reduction mapping . More recent models , namely Laplacian PLSI ( LapPLSI ) and Locally-consistent Topic Model ( LTM ) , have incorporated the local manifold structure into topic models and have shown the resulting benefits . But these approaches fall short of the full discriminating power of manifold learning as they only enhance the proximity between the low-rank representations of neighboring pairs without any consideration for non-neighboring pairs . In this paper , we propose Discriminative Topic Model ( DTM ) that separates non-neighboring pairs from each other in addition to bringing neighboring pairs closer together , thereby preserving the global manifold structure as well as improving the local consistency . We also present a novel model fitting algorithm based on the generalized EM and the concept of Pareto improvement . As a result , DTM achieves higher classification performance in a semi-supervised setting by effectively exposing the manifold structure of data . We provide empirical evidence on text corpora to demonstrate the success of DTM in terms of classification accuracy and robustness to parameters compared to state-of-the-art techniques .
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