广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (02): 101-107.doi: 10.12052/gdutxb.230001

• 计算机科学与技术 • 上一篇    

基于多核学习的单分类多示例学习算法

古慧敏1, 肖燕珊1, 刘波2   

  1. 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
  • 收稿日期:2023-01-03 发布日期:2024-04-23
  • 通信作者: 肖燕珊(1981-),女,教授,博士生导师,主要研究方向为机器学习、数据挖掘,E-mail:xiaoyanshan@189.cn
  • 作者简介:古慧敏(1998-),女,硕士研究生,主要研究方向为机器学习、数据挖掘,E-mail:1220186063@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61876044，62076074)

Multiple-kernel One-class Multiple-instance Learning Algorithm

Gu Hui-min1, Xiao Yan-shan1, Liu Bo2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-01-03 Published:2024-04-23

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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。

关键词: 多核学习, 单分类, 支持向量数据描述, 多示例学习

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.

Key words: multiple-kernel learning, one-class classification, support vector data description, multiple-instance learning

中图分类号: 

  • TP181
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[1] 蔡昊, 刘波. 半监督两个视角的多示例聚类模型[J]. 广东工业大学学报, 2021, 38(03): 22-28,47.
[2] 黎启祥, 肖燕珊, 郝志峰, 阮奕邦. 基于抗噪声的多任务多示例学习算法研究[J]. 广东工业大学学报, 2018, 35(03): 47-53.
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