Li Wan et. al. “A Hybrid Neural Network-Latent Topic Model”, JMLR 2012
This paper introduces a hybrid model that combines a neural network with a latent topic model.
This paper combine scale-invariant feature transform (SIFT), neural network, and Latent Dirichlet allocation (LDA) to perform scence classification, where the hybrid model is shown to outperform models based solely on neural networks or topic models. The main contribution is the way to combine neural network and LDA.
The neural work here acts as a trainable feature extractor to provide a low-dimensional embedding for the input data; while the topic model captures the group structure of the data.