Variational learning for the inverted beta-liouville mixture model and its application to text categorization

Yongfa Ling ^[1] ; Wenbo Guan ; Qiang Ruan ; Heping Song ^[2] ; Yuping Lai ^[3]
1. [1] Hezhou University
  
  Hezhou University
  
  China
2. [2] Jiangsu University
  
  Jiangsu University
  
  China
3. [3] Beijing University of Posts and Telecommunications
  
  Beijing University of Posts and Telecommunications
  
  China
Mostrar afiliaciones +
Localización: IJIMAI, ISSN-e 1989-1660, Vol. 7, Nº. 5, 2022, págs. 76-84
Idioma: inglés
Enlaces
- Texto completo (pdf)
Resumen
- The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an effective way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.