基于多核学习的单分类多示例学习算法

古慧敏; 肖燕珊; 刘波

doi:10.12052/gdutxb.230001

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基于多核学习的单分类多示例学习算法

计算机科学与技术 | 更新时间：2026-05-27

- 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
- 广东工业大学学报 2024年41卷第2期页码：101-107
- 作者机构：
  
  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
- 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
  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
- 基金信息：
  
  国家自然科学基金资助项目(61876044，62076074)
- DOI：10.12052/gdutxb.230001
  中图分类号： TP181
- 纸质出版：2024
- 稿件说明：
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古慧敏, 肖燕珊, 刘波. 基于多核学习的单分类多示例学习算法[J]. 广东工业大学学报, 2024, 41(2): 101-107.

Gu Hui-min, Xiao Yan-shan, Liu Bo. Multiple-kernel One-class Multiple-instance Learning Algorithm[J]. 2024, 41(2): 101-107.
古慧敏, 肖燕珊, 刘波. 基于多核学习的单分类多示例学习算法[J]. 广东工业大学学报, 2024, 41(2): 101-107. DOI： 10.12052/gdutxb.230001.

Gu Hui-min, Xiao Yan-shan, Liu Bo. Multiple-kernel One-class Multiple-instance Learning Algorithm[J]. 2024, 41(2): 101-107. DOI： 10.12052/gdutxb.230001.

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

Keywords

references

DIETTERICH T G, LATHROP R H, LOZANO-PÉREZ T. Solving the multiple instance problem with axis-parallel rectangles [J]. Artificial Intelligence, 1997, 89(1-2): 31-71.

LI Q X, XIAO Y S, HAO Z F, et al . An algorithm based on multi-instance anti-noise learning [J ] . Journal of Guangdong University of Technology, 2018, 35(3): 47-53.

CAI H, LIU B. A semi-supervised two-view multiple-instance clustering model [J]. Journal of Guangdong University of Technology, 2021, 38(3): 22-28,47.

SHANG J, HONG S, ZHOU Y, et al. Knowledge guided multi-instance multi-label learning via neural networks in medicines prediction[C] //Asian Conference on Machine Learning. Beijing: PMLR, 2018: 831-846.

YANG Y, TU Y, LEI H, et al . HAMIL: hierarchical aggregation-based multi-instance learning for microscopy image classification [J ] . Pattern Recognition, 2023, 136: 109245.

ZHA Y, ZHANG Y, KU T, et al . Multiple instance models regression for robust visual tracking [J ] . IEEE Transactions on Circuits and Systems for Video Technology, 2020, 31(3): 1125-1137.

XU K, ZHAO Z, GU J, et al. Multi-instance multi-label learning for gene mutation prediction in hepatocellular carcinoma[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) . Montreal: IEEE, 2020: 6095-6098.

ANDREWS S, TSOCHANTARIDIS I, HOFMANN T. Support vector machines for multiple-instance learning [J]. Advances in Neural Information Processing Systems, 2002, 15: 561-568.

SCHÖLKOPF B, WILLIAMSON R C, SMOLA A, et al . Support vector method for novelty detection [J ] . Advances in Neural Information Processing Systems, 1999, 12: 582-588.

TAX D M J, DUIN R P W. Support vector data description [J]. Machine Learning, 2004, 54(1): 45-66.

WANG X, ZHANG Z, MA Y, et al. One-class multiple instance learning via robust pca for common object discovery[C] //Asian Conference on Computer Vision. Heidelberg: Springer, 2012: 246-258.

XIAO Y, LIU B, HAO Z, et al . A similarity-based classification framework for multiple-instance learning [J ] . IEEE Transactions on Cybernetics, 2013, 44(4): 500-515.

CHEPLYGINA V, TAX D M J, LOOG M. Multiple instance learning with bag dissimilarities [J]. Pattern Recognition, 2015, 48(1): 264-275.

CHEN Y, BI J, WANG J Z. MILES: multiple-instance learning via embedded instance selection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 1931-1947.

SCHÖLKOPF B, SMOLA A, MÜLLER K. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation, 1998, 10(5): 1299-1319.

WANG H Q, SUN F C, CAI Y N, et al . On multiple kernel learning methods [J ] . Acta Automatica Sinica, 2010, 36(8): 1037-1050.

HONG S, CHAE J. Active learning with multiple kernels [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(7): 2980-2994.

GAUTAM C, TIWARI A. Localized multiple kernel support vector data description[C]//2018 IEEE International Conference on Data Mining Workshops (ICDMW) . Singapore: IEEE, 2018: 1514-1521.

SOHRAB F, RAITOHARJU J, IOSIFIDIS A, et al . Multimodal subspace support vector data description [J ] . Pattern Recognition, 2021, 110: 107648.

LU M, LIU L H, WU L H. Research on multi-kernel support vector data description method of classification [J]. Computer Engineering and Applications, 2016, 52(18): 68-73.

CHEN Y, ZHOU X S, HUANG T S. One-class SVM for learning in image retrieval[C]//Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205) . Thessaloniki: IEEE, 2001, 1: 34-37.

EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al . The pascal visual object classes (voc) challenge [J ] . International Journal of Computer Vision, 2010, 88(2): 303-338.

YUAN T, WAN F, FU M, et al. Multiple instance active learning for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Virtual: IEEE, 2021: 5330-5339.

DECENCIÈRE E, ZHANG X, CAZUGUEL G, et al . Feedback on a publicly distributed image database: the Messidor database [J ] . Image Analysis Stereology, 2014, 33(3): 231-234.

HAND D J, TILL R J. A simple generalisation of the area under the ROC curve for multiple class classification problems [J]. Machine Learning, 2001, 45: 171-186.

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