- Title
- Isolation set-kernel and its application to multi-instance learning
- Creator
- Xu, Bi-Cun; Ting, Kaiming; Zhou, Zhi-Hua
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/170469
- Identifier
- vital:14194
- Identifier
-
https://doi.org/10.1145/3292500.3330830
- Identifier
- ISBN:978-1-4503-6201-6
- Abstract
- Set-level problems are as important as instance-level problems. The core in solving set-level problems is: how to measure the similarity between two sets. This paper investigates data-dependent kernels that are derived directly from data. We introduce Isolation Set Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning. In contrast, most current set-similarities are not dependent on the underlying data distribution. We theoretically analyze the characteristic of Isolation Set-Kernel. As the set-kernel has a finite feature map, we show that it can be used to speed up the set-kernel computation significantly. We apply Isolation Set-Kernel to Multi-Instance Learning (MIL) using SVM classifier, and demonstrate that it outperforms other set-kernels or other solutions to the MIL problem.
- Publisher
- Assoc Computing Machinery
- Relation
- 25th ACM SIGKDD International Conferencce on Knowledge Discovery and Data Mining, KDD 2019; Anchorage, United States; 4th-8th August 2019 p. 941-949
- Rights
- Copyright © 2019 Association for Computing Machinery.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Data-dependent kernel; Feature map; SVM classifiers; Multi-Instance; Learning
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