Classification of Mass Spectrometric Data Based on Kernel Sliced Inverse Regression and Linear Discriminant Analysis

Extracting the most discriminatory features was important in mass spectrometry recognition tasks. In the case of a small number of mass spectrometric samples, the existed methods for extracting discriminatory features encountered various problems, such as the over-fitting, the multicollinearity within numerous predictor variables, nonlinear quantitative relationship between the structure and properties and etc. A novel classification method was constructed by combination of the kernel sliced inverse regression (KSIR) with linear discriminant analysis (LDA). The resulting discriminate model based on this proposed approach (KSIR-LDA) was divided into two steps: the first step was to perform the SIR algorithm in the reproducing kernel Hilbert space (RKHS) induced by applying the kernel trick to the original high dimensional mass spectrometric data for nonlinear dimension reduction and feature extraction, and the second step was that to build the LDA discriminate model in making use of the extracted feature variates. Finally, application to the soft drinks four-group classification problem was presented with a comparison to some other methods. The results show that it is an effective classification method in the structural risk minimization, non-linear characteristics, avoiding the over-fitting and strong generalization ability. At the same time, the KSIR-LDA discriminate model is more concise and can be used to observe the structure of the set of samples.