图 书 馆
REGO：基于欧氏图优化的Renyi信息秩估计REGO: Rank-based estimation of Renyi information using Euclidean graph optimization |
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| 课程网址: | http://videolectures.net/aistats2010_poczos_rrbe/ |
| 主讲教师: | Barnabás Póczos |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2010-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  我们提出了一种新方法，用于使用相应的算子对数据的归一化秩进行多变量分布，从而对多变量分布的Renyi和Shannon信息进行非参数估计。由于分布的信息与它的copula的负熵相同，因此我们的方法通过根据分布copcop的经验估计来解决欧几里德图优化问题来估计此信息。由于系动词的性质，我们表明所得的仁义信息估计量具有很强的一致性和鲁棒性。此外，除了模拟实验，我们还证明了其在图像配准中的适用性。 |
| 课程简介: | We propose a new method for a non-parametric estimation of Renyi and Shannon information for a multivariate distribution using a corresponding copula, a multivariate distribution over normalized ranks of the data. As the information of the distribution is the same as the negative entropy of its copula, our method estimates this information by solving a Euclidean graph optimization problem on the empirical estimate of the distribution's copula. Owing to the properties of the copula, we show that the resulting estimator of Renyi information is strongly consistent and robust. Further, we demonstrate its applicability in the image registration in addition to simulated experiments. |
| 关 键 词: | 欧氏图; 参数分析 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-13：zyk |
| 最后编审: | 2020-10-13：zyk |
| 阅读次数: | 34 |