Abstract
In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least squares support vector machine based on multikernel learning is proposed and applied to the recognition of epilepsy abnormal signals. The algorithm uses the style conversion matrix to represent the style information contained in the sample, regularizes it in the objective function, optimizes the objective function through the commonly used alternative optimization method, and simultaneously updates the style conversion matrix and classifier during the iteration process parameter. In order to use the learned style information in the prediction process, two new rules are added to the traditional prediction method, and the style conversion matrix is used to standardize the sample style before classification.
Highlights
Due to the proposal of support vector machine (SVM) [1] and the development of related theories, the kernel method has become an effective method to deal with nonlinear fractional data
Inspired by the above scholars, we propose style regularization least squares support vector machine based on multiple kernel learning (SR-MKL-SVM) to excavate and utilize the physical similarities between sample points and the implied style information in samples
Because the style information contained in the sample is used effectively in the training and prediction process, the experiments of most of the stylized data sets show that SR-MKL-SVM is relatively recent and the classical multikernel support vector machine algorithm is effective
Summary
Due to the proposal of support vector machine (SVM) [1] and the development of related theories, the kernel method has become an effective method to deal with nonlinear fractional data. The multikernel learning algorithm fully combines the mapping ability of different kernel functions for data, essentially, it only uses the physical characteristics of samples that include similarity and distance and fails to take into account the implicit information in the stylized data set in the real situation. In addition to using the physical characteristics of each basic kernel function for data mapping to express the similarity between samples, the algorithm uses the style transformation matrix to represent and mine the style information contained in the data set and takes it into the objective function. Because the style information contained in the sample is used effectively in the training and prediction process, the experiments of most of the stylized data sets show that SR-MKL-SVM is relatively recent and the classical multikernel support vector machine algorithm is effective
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