Open Access
ARTICLE
Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation
He Ni1,*, Yongqiao Wang1, Buyun Xu2
1 School of Finance, Zhejiang Gongshang University, Hangzhou, China
2 Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, Tonglu, China
* Corresponding Author: He Ni,
Intelligent Automation & Soft Computing 2020, 26(2), 227-236. https://doi.org/10.31209/2019.100000068
Abstract
The paper presents a probabilistic clustering approach based on self-organizing
learning algorithm and recursive Bayesian estimation. The model is built upon
the principle that the market data space is multimodal and can be described by
a mixture of Gaussian distributions. The model parameters are approximated by
a stochastic recursive Bayesian learning: searches for the maximum a posterior
solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile
likelihood confidence interval to avoid prolonged learning. The proposed model
is based on a number of pioneer works, such as Mixture Gaussian
Autoregressive Model, Self-Organizing Mixture Map, and have some favoured
attributes on its robust convergence and good generalization. The experimental
results on both artificial and real market data show that the algorithm is a good
alternative in measuring multimodal distribution.
Keywords
Cite This Article
H. Ni, Y. Wang and B. Xu, "Self-organizing gaussian mixture map based on adaptive recursive bayesian estimation,"
Intelligent Automation & Soft Computing, vol. 26, no.2, pp. 227–236, 2020. https://doi.org/10.31209/2019.100000068