Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (9): 1532-1544.DOI: 10.3778/j.issn.1673-9418.1906018

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

SHEN Hao, WANG Shitong   

  1. 1. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Key Laboratory of Media Design and Software Technology of Jiangsu Province, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2020-09-01 Published:2020-09-07



  1. 1. 江南大学 数字媒体学院,江苏 无锡 214122
    2. 江南大学 江苏省媒体设计与软件技术重点实验室,江苏 无锡 214122


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.

Key words: least squares support vector machines (LSSVM), multiple kernel learning, stylistic data, style information



关键词: 最小二乘支持向量机(LSSVM), 多核学习, 风格化数据, 风格信息