Key instance detection in multi-instance learning
The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags, whereas in many real applications, it is desirable to get the instance labels, especially the labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a problem has been...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/106222 http://hdl.handle.net/10220/23974 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags,
whereas in many real applications, it is desirable to get the instance labels, especially the
labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a
problem has been largely unexplored before. In this paper, we formulate the Key Instance
Detection (KID) problem, and propose a voting framework (VF) solution to KID. The key
of VF is to exploit the relationship among instances, represented by a citer kNN graph. This
graph is different from commonly used nearest neighbor graphs, but is suitable for KID.
Experiments validate the effectiveness of VF for KID. Additionally, VF also outperforms state-of-the-art MIL approaches on the performance of bag label prediction. |
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