南京大学学报（自然科学版）2026，Vol.62Issue(2)：297-308,12.DOI:10.13232/j.cnki.jnju.2026.02.012
用于多实例嵌入学习的层次化关键实例选择方法
Hierarchical key instance selection for multi-instance embedding learning
摘要
Abstract
In MIL(Multi-Instance Learning),data objects are hierarchically organized as bags containing multiple instances.The well-known MIL embedding approach embeds each bag as a vector by selecting representative instances.However,most existing methods ignore the hierarchical structure of bags,leading to the generated KIS(Key Instance Set)that contains substantial outlier instances(the instances where bag labeling cannot be triggered).Additionally,KIS is not utilized to exclude outliers in bags,which will impact embedding quality.To address these issues,we propose HKMIL(Hierarchical Key Instance Selection for Multi-Instance Embedding Learning)algorithm with three technologies.First,HIS(Hierarchical Instance Selection)uses subspace-and affinity-based updates to identify and refine KIS,generating new bags while considering instance density.Second,FVE(Fisher Vector Embedding)technique uses Gaussian mixture models to extract key statistical information from the new bags,converting them into vectors to simplify the MIL problem.Third,ECT(Ensemble Classification Technique)dynamically weights the information before and after KIS updates for improved bag label predictions.Experiments on six MIL tasks show that HKMIL outperforms nine state-of-the-art algorithms,achieving superior classification performance.关键词
多实例学习/关键实例/实例选择/嵌入方法/集成学习Key words
multi-instance learning/key instance/instance selection/embedding/ensemble learning分类
信息技术与安全科学引用本文复制引用
潘臻,张雨轩,张佳慧,闵帆,杨梅..用于多实例嵌入学习的层次化关键实例选择方法[J].南京大学学报（自然科学版）,2026,62(2):297-308,12.基金项目
成都师范学院科研项目(YJRC202449),南充市政府高校科研合作项目(23XNSYSX0084,23XNSYSX0062),浙江省海洋大数据挖掘与应用重点实验室开放课题(OBDMA202102) （YJRC202449）
