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
古慧敏 1肖燕珊 1刘波2
作者信息
- 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
- 折叠
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.
Abstract
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 multiple-kernel 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Key words
multiple-kernel learning/one-class classification/support vector data description/multiple-instance learning引用本文复制引用
基金项目
国家自然科学基金(61876044)
国家自然科学基金(62076074)
出版年
2024
国家科技期刊平台
NSTL
万方数据