title: Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.
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 (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.
authors.forename: Bing
authors.initials: B
authors.lastname: Li
authors.forename: Chunfeng
authors.initials: C
authors.lastname: Yuan
authors.forename: Weihua
authors.initials: W
authors.lastname: Xiong
authors.forename: Weiming
authors.initials: W
authors.lastname: Hu
authors.forename: Houwen
authors.initials: H
authors.lastname: Peng
authors.forename: Xinmiao
authors.initials: X
authors.lastname: Ding
authors.forename: Steve
authors.initials: S
authors.lastname: Maybank