Style Regularized Least Squares Support Vector Machine Based on Multiple Kernel Learning

• Main Author: SHEN Hao, WANG Shitong
• Format: Article
• Language: zho
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-09-01
• Series: Jisuanji kexue yu tansuo
• Subjects: least squares support vector machines (lssvm); multiple kernel learning; stylistic data; style information
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2358.shtml

Though current multiple kernel learning algorithms integrate the abilities of different kernel functions on the representation of the physical features of data, they do not make full use of the style information existing in the stylistic dataset. Therefore, a style regularized least squares support vector machine based on multiple kernel learning (MK-SRLSSVM) is proposed. Its basic idea is to consider the style transformation matrices which represent the style information of samples and it is regularized in the objective function. The commonly-used alternating optimization technique is utilized to optimize the objective function. Style transformation matrices and classifier parameters are simultaneously updated during iteration. In order to use the trained style information in the prediction process, two new rules are considered in the traditional prediction model. The unknown patterns are normalized by the learned style transform matrix before being classified. The proposed classifier not only keeps the advantages of existing multi-kernel learning algorithms in representing the physical features of samples, but also exploits the style information contained in the stylistic dataset so as to improve the classification performance effectively. The experimental results on the stylistic datasets confirm the effectiveness of the proposed classifier.