TY - JOUR
KW - Bayesian Inference
KW - Extended Variational Inference
KW - Mixture Model
KW - Text Categorization
KW - Inverted Beta-Liouville Distribution
AU - Yongfa Ling
AU - Wenbo Guan
AU - Qiang Ruan
AU - Heping Song
AU - Yuping Lai
AB - 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.
IS - Special Issue on Multimedia Streaming and Processing in Internet of Things with Edge Intelligence
M1 - 5
N2 - 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.
PY - 2022
SP - 76
EP - 84
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
UR - https://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_9.pdf
VL - 7
SN - 1989-1660
ER -