Zivkovic Zoran, van der Heijden Ferdinand
Laboratory for Measurement and Instrumentation, University of Twente, Enschede, The Netherlands.
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):651-6. doi: 10.1109/TPAMI.2004.1273970.
There are two open problems when finite mixture densities are used to model multivariate data: the selection of the number of components and the initialization. In this paper, we propose an online (recursive) algorithm that estimates the parameters of the mixture and that simultaneously selects the number of components. The new algorithm starts with a large number of randomly initialized components. A prior is used as a bias for maximally structured models. A stochastic approximation recursive learning algorithm is proposed to search for the maximum a posteriori (MAP) solution and to discard the irrelevant components.
当使用有限混合密度对多变量数据进行建模时，存在两个未解决的问题：组件数量的选择和初始化。在本文中，我们提出了一种在线（递归）算法，该算法可估计混合模型的参数并同时选择组件数量。新算法从大量随机初始化的组件开始。先验被用作最大结构化模型的偏差。提出了一种随机近似递归学习算法来搜索最大后验（MAP）解并丢弃无关组件。
