File Download
Supplementary
-
Citations:
- Appears in Collections:
Conference Paper: Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
Title | Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models |
---|---|
Authors | |
Issue Date | 2019 |
Publisher | PMLR. The Journal's web site is located at http://proceedings.mlr.press/ |
Citation | The 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 10-15 June 2019. In Proceedings of Machine Learning Research (PMLR), 2019, v. 97, p. 7463-7471 How to Cite? |
Abstract | We propose a fixed-point iteration approach to the maximum likelihood estimation for the incomplete multinomial model, which provides a unified framework for ranking data analysis. Incomplete observations typically fall in a subset of categories, and thus cannot be distinguished as belonging to a unique category. We develop a minorization–maximization (MM) type of algorithm, which requires relatively fewer iterations and shorter time to achieve convergence. Under such a general framework, incomplete multinomial models can be reformulated to include several well-known ranking models as special cases, such as the Bradley–Terry, Plackett–Luce models and their variants. The simple form of iteratively updating equations in our algorithm involves only basic matrix operations, which makes it efficient and easy to implement with large data. Experimental results show that our algorithm runs faster than existing methods on synthetic data and real data. |
Persistent Identifier | http://hdl.handle.net/10722/279400 |
ISSN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, C | - |
dc.contributor.author | Yin, G | - |
dc.date.accessioned | 2019-11-01T07:16:37Z | - |
dc.date.available | 2019-11-01T07:16:37Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | The 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 10-15 June 2019. In Proceedings of Machine Learning Research (PMLR), 2019, v. 97, p. 7463-7471 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | http://hdl.handle.net/10722/279400 | - |
dc.description.abstract | We propose a fixed-point iteration approach to the maximum likelihood estimation for the incomplete multinomial model, which provides a unified framework for ranking data analysis. Incomplete observations typically fall in a subset of categories, and thus cannot be distinguished as belonging to a unique category. We develop a minorization–maximization (MM) type of algorithm, which requires relatively fewer iterations and shorter time to achieve convergence. Under such a general framework, incomplete multinomial models can be reformulated to include several well-known ranking models as special cases, such as the Bradley–Terry, Plackett–Luce models and their variants. The simple form of iteratively updating equations in our algorithm involves only basic matrix operations, which makes it efficient and easy to implement with large data. Experimental results show that our algorithm runs faster than existing methods on synthetic data and real data. | - |
dc.language | eng | - |
dc.publisher | PMLR. The Journal's web site is located at http://proceedings.mlr.press/ | - |
dc.relation.ispartof | Proceedings of Machine Learning Research (PMLR) | - |
dc.relation.ispartof | Proceedings of the Thirty-sixth International Conference on Machine Learning | - |
dc.title | Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.hkuros | 308308 | - |
dc.identifier.hkuros | 308615 | - |
dc.identifier.volume | 97 | - |
dc.identifier.spage | 7463 | - |
dc.identifier.epage | 7471 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2640-3498 | - |