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统计依赖度量

Measures of Statistical Dependence
课程网址: http://videolectures.net/mlss06au_gretton_msd/  
主讲教师: Arthur Gretton
开课单位: 伦敦大学学院
开课时间: 2019-07-11
课程语种: 英语
中文简介:
信号处理中的许多重要问题取决于统计依赖性的度量。例如,在瞬时ICA的情况下,这种依赖性被最小化,其中线性混合信号使用它们(假定的)成对彼此独立来分离。已经提出了许多方法来测量这种依赖性,但是它们通常假定产生观察的密度的特定参数模型。最近的工作表明,核方法可用于找到根据它们比较的信号进行调整的估计值。目前正在改进这些方法,以提高精度,并允许随时间使用信号特性以改善信号可分离性。此外,这些方法可以应用于必须最大化观察之间的统计依赖性的情况,这对于某些类别的聚类算法是正确的。
课程简介: A number of important problems in signal processing depend on measures of statistical dependence. For instance, this dependence is minimised in the context of instantaneous ICA, in which linearly mixed signals are separated using their (assumed) pairwise independence from each other. A number of methods have been proposed to measure this dependence, however they generally assume a particular parametric model for the densities generating the observations. Recent work suggests that kernel methods may be used to find estimates that adapt according to the signals they compare. These methods are currently being refined, both to yeild greater accuracy, and to permit the use of the signal properties over time in improving signal separability. In addition, these methods can be applied in cases where the statistical dependence between observations must be maximised, which is true for certain classes of clustering algorithms.
关 键 词: 信号处理; 统计依赖性; 线性混合信号
课程来源: 视频讲座网
最后编审: 2019-07-10:lxf
阅读次数: 118