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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
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| 英文论文题目: |
Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
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| 论文题目英文: |
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| 作者: |
Li, Bing
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 年: |
2017
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| 卷: |
39 |
| 期: |
12 |
| 页: |
2554-2560 |
| 联系作者: |
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| 影响因子: |
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| 摘要: |
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm ((MIL)-I-2) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse epsilon-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL. Experiments and analyses in many practical applications prove the effectiveness of the M2IL. |
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