图 书 馆
用高斯混合模型估计EM算法的局部最优值Estimating Local Optimums in EM Algorithm over Gaussian Mixture Model |
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| 课程网址: | http://videolectures.net/icml08_zhang_elo/ |
| 主讲教师: | Zhenjie Zhang |
| 开课单位: | 新加坡国立大学 |
| 开课时间: | 2008-08-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | EM algorithm is a very popular method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum. Therefore, it is usually required to run multiple procedures of EM algorithm with different initial configurations and return the best solution. To improve the efficiency of this scheme, we propose a new method which can estimate an upper bound on the logarithm likelihood of the local optimum, based on the current configuration after the latest EM iteration. This is accomplished by first deriving some region bounding the possible locations of local optimum, followed by some upper bound estimation on the maximum likelihood. With this estimation, we can terminate an EM algorithm procedure if the estimated local optimum is definitely worse than the best solution seen so far. Extensive experiments show that our method can effectively and efficiently accelerate conventional EM algorithm. |
| 关 键 词: | 高斯混合模型; 对数似然; 局部最优 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 71 |