Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection
Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection
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DOI：
10.1142/s0218001405004228
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发表时间：
2005-08-01
影响因子：
1.5
通讯作者：
Cheng, QS
中科院分区：
文献类型：
--
作者：
Ma, JW;Gao, B;Cheng, QS
在贝叶斯阴阳（BYY）和谐学习理论下，在高斯混合混合BYY系统的双向架构上建立了一个和谐函数，其重要特征是，通过一般梯度规则使其最大化，可以在高斯混合混合的一组样本数据的参数学习过程中自动进行模型选择。本文进一步提出了共轭规则和自然梯度规则来有效地实现高斯混合函数的调和函数最大化，即BYY调和学习。仿真实验表明，这两种新的梯度规则不仅具有良好的性能，而且收敛速度比一般的梯度规则更快。
Under the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones.