Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning

摘要

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in manyudapplications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overalludperformance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-viewudmulti-instance learning algorithm (M2IL) that combines multiple context structures in a bag into a unified framework. The novel aspectsudare: (i) we propose a sparse "-graph model that can generate different graphs with different parameters to represent various contextudrelations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bagudclassification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cuesudfrom all views simultaneously to improve the discrimination of the M2IL. Experiments and analyses in many practical applications proveudthe effectiveness of the M2IL.