Enhanced Sparse based Discriminative Topic Representation for Text Categorization
Wenbin Zheng, Wenxu Zhang, Xi Yue, Yan Gao, Hong Tang · 2019
This paper presents an enhanced sparse based discriminative topic representation method for text categorization. By constructing category center vectors and combining with the latent Dirichlet allocation model, a more discriminative dictionary is obtained, which can describe the relationship between topic and word well. Furthermore, an enhanced sparse representation of documents can be generated with a L1/2 regularization in order to achive a good relationship between document and topic. The experimental results show that our proposed approach achieves more stable classification performance and obtains more high sparse degree.