Multiple-Instance feature extraction at the bag and instance levels using the maximum trace-difference criterion

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• 作者：Jing Chai^a ; ^{jingchai@aliyun.com} ; ^{jing_chai@hotmail.com} ; Bo Chen^b ; Fan Liu^a ; Zehua Chen^a ; Xinghao Ding^c
• 关键词：Multiple-instance learning ; Feature extraction ; Dimensionality reduction ; Maximum trace-difference
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：385-386
• 期：Complete
• 页码：353-377
• 全文大小：26923 K
• 卷排序：385

文摘

Multiple-Instance Learning (MIL) refers to the problem wherein each object is a bag consisting of multiple instances and only the bags' labels are provided. MIL data can contain irrelevant, redundant, and noisy components, which makes feature-extraction preprocessing essential for performance improvement. In this paper, we propose a Multiple-Instance Feature Extraction (MIFE) framework to design algorithms at both the bag and instance levels based on the Maximum Trace-Difference criterion, which simultaneously maximizes between-class scattering and minimizes within-class scattering. MIFE not only treats the existing Multiple-Instance Discriminant Analysis algorithm as an instance-level realization but also enables us to adopt different bag-level distances to design corresponding bag-level algorithms. In particular, we introduce the Class-to-Bag (C2B) and Bag-to-Bag (B2B) distances into the MIFE framework and obtain the MIFE-C2B and MIFE-B2B algorithms, respectively. The experimental results show that both MIFE-C2B and MIFE-B2B obtain competitive classification performance, and MIFE-B2B obtains the best performance on most tested datasets. The dimensionality reduction results show that both MIFE-C2B and MIFE-B2B obtain their best performance with no more than approximately 30% of the original dimensions on most tested datasets.