Graph Regularized Nonnegative Matrix Factorization with Longtail Constraint How to dig out long tail topics is a great challenge in text mining. In previous research, most of nonhierarchical topic models were based on a hypothesis that the topics in documents follow polynomial distribution, ignoring the topics at the tail of distribution curve. Hierarchical topic model have the ability to mine long tail topics by introducing the hierarchical relationship among topics, but leading to a higher computational complexity. In this article, we propose a new method to mine long tail topics, which is called graph regularized nonnegative matrix factorization with longtail constraint. It uses KL divergence to measure the difference between matrices, and use neighbor graph to preserve the intrinsic geometrical and discriminating structure between original samples in lowdimensional space. Experiment shows, the algorithm we proposed can mine more long tail topic information in document, and make improvement in the task of data mining, comparing to other method, such as classical dirichlet distribution, nonnegative matrix, hierarchical matrix, hierarchical latent dirichlet distribution.

Learning Short Text Representation using NonNegative Matrix Factorization and Word Semantic Correlations With the emergence of online medias, short texts have been increasingly available. Because the short texts have a limited contextual information, they are sparse, noisy and ambiguous. Conventional models to text representation are hence not applicable. In this paper, we propose a representation learning model for short texts to tackle this challenge. Our proposed model is based on NonNegative Matrix Factorization (NNMF). Due to the problem lacking word cooccurrences information, it effectively learns the representation model by incorporating the semantic relationship between words generated by unsupervised learning methods. Finally, the NNMFbased model is effectively solved by using a gradient descentbased algorithm. Experimental results on two benchmark data sets, including sentiment140 and 20newsgroups, demonstrate that our proposed approach achieved the improvement of classification accuracy to stateoftheart topic discovery techniques.