Abstract
Partial least squares method has many advantages in multivariate linear regression modeling, but its internal cross-checking method will lead to a sharp reduction of the principal component, thereby reducing the accuracy of the regression equation, and the selection of principal components about the traditional Chinese medicine data is particularly sensitive. This paper proposes a kind of partial least squares method based on deep belief nets. This method mainly uses the deep learning model to extract the upper-level features of the original data, putting the extracted features into the partial least squares model for multiple linear regression and evading the problem that selects the number of principal components, continuously adjusting the model parameters until satisfied well-pleased accuracy condition. Using Dachengqitang experimental data and data sets in the UCI Machine Learning Repository, the experimental results show that the partial least squares analysis method based on deep belief nets has good adaptability to TCM data.
Highlights
Partial least squares [7] is a new multivariate statistical data analysis method
Regression modeling can be carried out when the number of sample points is less than the number of variables or multiple correlations exist between variables, and the regression coefficient of each variable is easy to explain
Erefore, the work of this paper is improved based on the above partial least squares method: (1) e experimental data of the traditional Chinese medicine: there are often many variables related to the dependent variable, and even the observation data of some independent variables are costly, but they can be collected by leading-edge equipment
Summary
Root mean square error (RMSE) and redetermination coefficient (R2), it is judged whether the model is satisfied with the precision requirement at this time. E model retains the characteristic that PLS can solve the problem of multiple correlations of traditional Chinese medicine data, while avoiding the defect of cross-validity of PLS. It first performs canonical correlation analysis and multiple linear regression and restores the regression equation of the original variables. 5. Experiment Results and Analysis e experimental data in this paper are mainly from Dachengqitang experimental data (DCQT) of the Key Laboratory of Jiangxi University of Traditional Chinese Medicine and Housing, AirQuality, and CBM [12] on UCI data sets. T sigmoid(Eo × Wt + at), U sigmoid(Fo × Wu + au), put the PLS outer model into multiple linear regression, and find the DBN-PLS equation.
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