摘要： 偏最小二乘法内部采用主成分分析,不能充分表达数据的非线性特征,对非线性数据的预测精度较低。为此,提出一种融合受限玻尔兹曼机与偏最小二乘的分析预测方法。该方法利用受限玻尔兹曼机对特征空间提取非线性结构,将提取的特征成分取代偏最小二乘中的成分,从而得到适应非线性的模型。实验结果表明,融合受限玻尔兹曼机与偏最小二乘法的分析方法能较好地反映数据的非线性特征。

关键词: 受限玻尔兹曼机, 偏最小二乘法, 非线性, 传统中医药信息, 主成分分析

Abstract: Partial Least Squares(PLS) method adopts Principal Component Analysis(PCA),it cannot express the nonlinear characteristic,and the prediction accuracy is low in the nonlinear data.Based on this,an analysis and predicting method combining Restricted Boltzmann Machine(RBM) with PLS is proposed.The method can extract nonlinear structure of feature space by RBM and replace the components in PLS with the extracted components,forming a model which can adapt to nonlinear dose-effect relationship.Experimental results show that the method can well reflect the nonlinear characteristics of data.

Key words: Restricted Boltzmann Machine(RBM), Partial Least Squares(PLS) method, nonlinear, Traditional Chinese Medicine(TCM)information, Principal Component Analysis(PCA)

中图分类号: 

• TP18