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...
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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...
详细信息
ISBN:
(数字)9781728152561
ISBN:
(纸本)9781728152578
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.
In view of the lack of big data access platform in current manufacturing industry, the big data access platform was studied based on SOA. With the analysis of big data characteristics and the big data access business ...
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