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<inline-formula><tex-math notation="LaTeX">$^2$ </tex-math><alternatives><inline-graphic xlink:href="li-ieq1-2669303.gif"/></alternatives></inline-formula>IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math><alternatives> <inline-graphic xlink:href="li-ieq2-2669303.gif"/></alternatives></inline-formula>-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<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math><alternatives><inline-graphic xlink:href="li-ieq3-2669303.gif"/></alternatives></inline-formula>IL. Experiments and analyses in many practical applications prove the effectiveness of the M<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math><alternatives><inline-graphic xlink:href="li-ieq4-2669303.gif"/> </alternatives></inline-formula>IL.