The BYY annealing learning algorithm for Gaussian mixture with automated model selection
The BYY annealing learning algorithm for Gaussian mixture with automated model selection
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DOI：
10.1016/j.patcog.2006.12.028
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Jinwen Ma;Jianfeng Liu
中科院分区：
文献类型：
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作者：
Jinwen Ma;Jianfeng Liu
贝叶斯阴阳（BYY）学习提供了一种新机制，通过最大化高斯混合 BYY 系统后向架构上的和谐函数，实现自动模型选择的参数学习。然而，由于和谐函数存在大量的局部最大值，因此任何局部搜索算法（例如硬切EM算法）都不能很好地工作。为了克服这个困难，我们提出了一种模拟退火学习算法来搜索和谐函数的全局最大值，并表示为一种确定性退火EM过程。仿真实验表明，该BYY退火学习算法能够在学习过程中高效、自动地确定簇数或高斯数。此外，BYY退火学习算法成功应用于两个现实数据集，包括虹膜数据分类和无监督彩色图像分割。
Bayesian Ying–Yang (BYY) learning has provided a new mechanism that makes parameter learning with automated model selection via maximizing a harmony function on a backward architecture of the BYY system for the Gaussian mixture. However, since there are a large number of local maxima for the harmony function, any local searching algorithm, such as the hard-cut EM algorithm, does not work well. In order to overcome this difficulty, we propose a simulated annealing learning algorithm to search the global maximum of the harmony function, being expressed as a kind of deterministic annealing EM procedure. It is demonstrated by the simulation experiments that this BYY annealing learning algorithm can efficiently and automatically determine the number of clusters or Gaussians during the learning process. Moreover, the BYY annealing learning algorithm is successfully applied to two real-life data sets, including Iris data classification and unsupervised color image segmentation.