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|a 10.1109/TPAMI.2017.2669303
|2 doi
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|a pubmed24n0896.xml
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|a eng
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| 100 |
1 |
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|a Li, Bing
|e verfasserin
|4 aut
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| 245 |
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|a Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 17.12.2018
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|a Date Revised 17.12.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -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 MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Yuan, Chunfeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiong, Weihua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Weiming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Peng, Houwen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ding, Xinmiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Maybank, Steve
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 12 vom: 16. Dez., Seite 2554-2560
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|g year:2017
|g number:12
|g day:16
|g month:12
|g pages:2554-2560
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|u http://dx.doi.org/10.1109/TPAMI.2017.2669303
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