自动化学报2016，Vol.42Issue(4)：566-579,14.DOI:10.16383/j.aas.2016.c150255
稀疏贝叶斯混合专家模型及其在光谱数据标定中的应用
Sparse Bayesian Mixture of Experts and Its Application to Spectral Multivariate Calibration
摘要
Abstract
In spectral multivariate calibration, high dimensional spectral data are often measured on different conditions. To predict the property value of a spectrum without knowing its source, a new sparse Bayesian mixture experts (ME) model is proposed and applied to the multivariate calibration model for selecting the sparse features. The technique of mixture of experts can divide the training data into some different classes and estimate the different predictive functions for each class. Therefore, ME is suitable for prediction of multiple-source spectral data. To analyze high dimensional spectral data, the new ME model employs the sparse Bayesian method to select certain features without tuning parameters. Moreover, the multinomial probit model is used as the gate function to obtain the analytic posterior distribution in this model. This new method is compared with some classical multivariate calibration methods on artificial and some real-world datasets. Experimental results show the advantage of proposed model for high dimensional spectral data.关键词
多元校正/混合专家模型/特征提取/变分推断Key words
Multivariate calibration/mixture of experts/feature selection/variational inference引用本文复制引用
俞斌峰,季海波..稀疏贝叶斯混合专家模型及其在光谱数据标定中的应用[J].自动化学报,2016,42(4):566-579,14.基金项目
国家高技术研究发展计划(863计划)(AA2100100021)资助Supported by National High Technology Research and Devel-opment Program of China (863 Program)(AA2100100021) （863计划）
