Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning

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 (M
$^2$
IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse
$\varepsilon$
-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 M
$^2$
IL. Experiments and analyses in many practical applications prove the effectiveness of the M
$^2$
IL.