Li, B. and Yuan, C. and Xiong, W. and Hu, W. and Peng, H. and Ding, X. and Maybank, Stephen J. (2017) Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12), pp. 2554-2560. ISSN 0162-8828.
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Abstract
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 (M2IL) 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 M2IL. Experiments and analyses in many practical applications prove the effectiveness of the M2IL.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Keyword(s) / Subject(s): | multi-instance learning, multi-view, sparse representation, dictionary learning |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 13 Feb 2017 09:31 |
| Last Modified: | 09 Aug 2023 12:41 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/18136 |
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