Mixture of Softmax sLDA

In this paper, we propose a new variant of supervised Latent Dirichlet Allocation(sLDA): mixture of soft max sLDA, for image classification. Ensemble classification methods can combine multiple weak classifiers to construct a strong classifier. Inspired by the ensemble idea, we try to improve sLDA model using the idea. The mixture of soft max model is a probabilistic ensemble classification model, it can fit the training data and class label well. We embed the mixture of soft max model into LDA model under the framwork of sLDA, and construct an ensemble supervised topic model for image classification. Meanwhile, we derive an elegant parameters estimation algorithm based on variational EM method, and give a simple and efficient approximation method for classifying a new image. Finally, we demonstrate the effectiveness of our model by comparing with some existing approaches on two real world datasets. The results show that our model enhances classification accuracy by 7% on the 1600-image Label Me dataset and 9% on the 1791-image UIUC-Sport dataset.