Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to 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 canno...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2022-09-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3157 |
| _version_ | 1811266091549196288 |
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| author | Yongfa Ling Wenbo Guan Qiang Ruan Heping Song Yuping Lai |
| author_facet | Yongfa Ling Wenbo Guan Qiang Ruan Heping Song Yuping Lai |
| author_sort | Yongfa Ling |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T20:35:51Z |
| format | Article |
| id | doaj.art-3ee46c19b2e54a24afcd488b8fc932e5 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 |
| language | English |
| last_indexed | 2024-04-12T20:35:51Z |
| publishDate | 2022-09-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-3ee46c19b2e54a24afcd488b8fc932e52022-12-22T03:17:35ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602022-09-0175768410.9781/ijimai.2022.08.006ijimai.2022.08.006Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text CategorizationYongfa LingWenbo GuanQiang RuanHeping SongYuping LaiThe 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.https://www.ijimai.org/journal/bibcite/reference/3157bayesian inferenceextended variational inferencemixture modeltext categorizationinverted beta-liouville distribution |
| spellingShingle | Yongfa Ling Wenbo Guan Qiang Ruan Heping Song Yuping Lai Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization International Journal of Interactive Multimedia and Artificial Intelligence bayesian inference extended variational inference mixture model text categorization inverted beta-liouville distribution |
| title | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization |
| title_full | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization |
| title_fullStr | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization |
| title_full_unstemmed | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization |
| title_short | Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization |
| title_sort | variational learning for the inverted beta liouville mixture model and its application to text categorization |
| topic | bayesian inference extended variational inference mixture model text categorization inverted beta-liouville distribution |
| url | https://www.ijimai.org/journal/bibcite/reference/3157 |
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