融入深度学习的偏最小二乘优化方法

Optimization method of integrate deep learning into PLS

偏最小二乘在多元变量分析中得到了广泛的应用。但偏最小二乘方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。提出了一种融入深度学习的偏最小二乘优化方法,该方法利用深度学习的稀疏自编码器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,从而形成能适应非线性的模型。分别采用大承气汤、麻杏石甘汤、葛根芩连汤和UCI数据集的数据进行分析处理,实验结果表明,融入深度学习的偏最小二乘优化方法能较好地反映中医药数据的特征。

Partial least squares has been widely used in the multiple variable analysis. However, partial least squares method using principal component analysis, cannot express the nonlinear characteristic, and accuracy is low in the nonlinear data. Therefore, this paper proposed an analysis and predicting method of deep learning combining with PLS. The method could extract nonlinear structure of feature space by sparse autoencoder of deep learning and replace the components in PLS with the extracted components, forming a model which could adapt to nonlinear dose-effect relationship. It analyzed and processed the data of large chengqi decoction, ma xing shi gan tang, puerariae and seutellariae and eoptidis decoction, UCI data set. The experimental results show that, the deep learning and PLS method can well reflect the characteristics of TCM data.