Presentation | 2014-03-07 Parameter estimation for von Mises-Fisher mixture model via Gaussian distribution Suguru YASUTOMI, Toshihisa TANAKA, |
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Abstract(in English) | Statistics that deals with direction, such as angles, phases and wind directions is directional statistics. In directional statistics, a von Mises-Fisher (vMF) distribution is an important distribution. A maximum likelihood estimator and a variational Bayes estimator for a vMF mixture model have already been derived. However, the maximum likelihood estimator may accumulate approximation error during an iterative algorithm. Besides, the variational Bayes estimator cannot estimate some parameters. This article derives an estimator of the parameters in the vMF mixture model via the Gaussian distribution to solve these problems. We focus on the fact that the vMF distribution is derived from the Gaussian distribution. At first, we apply the estimation for the Gaussian mixture model to the data. Then, we convert the estimated Gaussian mixture distribution to a vMF mixture distribution. Experimental results support the analysis. |
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Paper # | CAS2013-129,SIP2013-175,CS2013-142 |
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Committee | CS |
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Conference Date | 2014/2/27(1days) |
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Registration To | Communication Systems (CS) |
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Language | JPN |
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Title (in English) | Parameter estimation for von Mises-Fisher mixture model via Gaussian distribution |
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1st Author's Name | Suguru YASUTOMI |
1st Author's Affiliation | Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology() |
2nd Author's Name | Toshihisa TANAKA |
2nd Author's Affiliation | Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology |
Date | 2014-03-07 |
Paper # | CAS2013-129,SIP2013-175,CS2013-142 |
Volume (vol) | vol.113 |
Number (no) | 465 |
Page | pp.pp.- |
#Pages | 6 |
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