Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
This is an incremental improvement for researchers in machine learning and text analysis, addressing a specific computational bottleneck in mixture models.
The paper tackled the intractable moment computation in the variational inference of the Inverted Beta-Liouville Mixture Model by proposing a new function under the extended variational inference framework, enabling analytically tractable solutions and demonstrating good performance in experiments with synthesized data and text categorization.
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 elegant way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.