硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Gaussian mixture model with local consistency: a hierarchical minimum message length-based approach
影响因子：4.5
DOI码：10.1007/s13042-023-01910-w
所属单位：西南交通大学
发表刊物：International Journal of Machine Learning and Cybernetics
刊物所在地：GERMANY
关键字：Gaussian mixture models; Minimum message length criterion; Hierarchical structure; Covariance matrix; Graph Laplacian
摘要：Gaussian mixture model (GMM) is widely used in many domains, e.g. data mining. The unsupervised learning of the finite mixture (ULFM) model based on the minimum message length (MML) criterion for mixtures enables adaptive model selection and parameter estimates. However, some datasets have a hierarchical structure. If the MML criterion does not consider the hierarchical structure of the a priori, the a priori coding length in the criterion is inaccurate. It is difficult to achieve a good trade-off between the model’s complexity and its goodness of fitting. Therefore, a locally consistent GMM with the hierarchical MML criterion (GM-HMML) algorithm is proposed. Firstly, the MML criterion determines the mixing probability (annihilation of components). To accurately control the competition between these relative necessary components, a hierarchical MML is proposed. Secondly, the hierarchical MML criterion is regularized using the graph Laplacian. The manifold structure is incorporated into the parameter estimator to avoid possible overfitting problems caused by the fine-grained prior. The presented MML criterion enhances the degree of component annihilation, which not only does not annihilate the necessary components but also reduces the iterations. The proposed approach is testified on the real datasets and achieves good model order and clustering accuracy.
合写作者：Zeng Yu,Hongjun Wang,Jihong Wan,Tianrui Li
第一作者：Min Li
论文类型：SCI
通讯作者：Guoyin Wang
学科门类：工学
文献类型：J
页面范围：1-20
ISSN号：1868-8071
是否译文：否
发表时间：2023-08-03
收录刊物：SCI