基于多核学习的单分类多示例学习算法  

Multiple-kernel One-class Multiple-instance Learning Algorithm

作　　者：古慧敏 肖燕珊[1] 刘波[2] Gu Hui-min;Xiao Yan-shan;Liu Bo(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China;School of Automation,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广东广州510006 [2]广东工业大学自动化学院,广东广州510006

出　　处：《广东工业大学学报》2024年第2期101-107,共7页Journal of Guangdong University of Technology

基　　金：国家自然科学基金资助项目(61876044,62076074)。

摘　　要：将多核学习引入到单分类多示例学习中,提出了一种基于多核学习的单分类多示例支持向量数据描述算法,解决了多核学习方法在实际应用中多示例数据具有比较复杂分布结构的学习问题。本文算法是将多个示例数据通过多个不同的核函数多核映射到特征空间,在特征空间中通过支持向量数据描述算法构建球形分类器。该算法采用迭代优化框架,首先,根据初始化包中的正示例来优化目标函数以此建立分类器。然后,根据上一步得到的分类器再对包中的正示例的标签进行更新。最后,在Corel、VOC 2007和Messidor数据集上的实验结果表明,所提出的算法比单核多示例方法具有更好的性能,进一步验证了算法的可行性和有效性。By introducing multiple-kernel into one-class multiple-instance learning,this paper proposes a novel multiple-kernel one-class multiple-instance learning based on support vector data description,which aims to solve the problem of multiple-kernel learning of multiple-instance data with a relatively complex distribution structure in practical applications.This algorithm maps multiple-instance data into the feature space through different multiplekernel functions,and constructs a spherical classifier by using support vector data description algorithm.To iteratively optimize the proposed algorithm adopts an iterative optimization framework,we first initialize the instances in positive bags as positive,and optimize the objective function to build up the classifier.Then,the labels of the positive instances in each bag are updated according to the classifier obtained in the previous step.The experimental results on the Corel,VOC 2007 and Messidor datasets show that the proposed algorithm achieves significantly better classification performance than state-of-the-art methods,demonstrating its feasibility and effectiveness.

关 键 词：多核学习 单分类 支持向量数据描述 多示例学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]