IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2554-2560. doi: 10.1109/TPAMI.2017.2669303. Epub 2017 Feb 14.
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.
在多示例学习 (MIL) 中，袋中实例之间的关系在许多应用中传递着重要的上下文信息。之前关于 MIL 的研究要么忽略了这些关系，要么简单地用固定的图结构来建模，从而导致在复杂环境下整体性能不可避免地下降。为了解决这个问题，本文提出了一种新的多视图多示例学习算法 (MIL)，该算法将袋中的多个上下文结构组合成一个统一的框架。新颖之处在于：（i）我们提出了一种稀疏图模型，该模型可以生成具有不同参数的不同图，以表示袋中不同的上下文关系；（ii）我们提出了一种多视图联合稀疏表示，将这些图集成到一个统一的框架中，用于袋分类；（iii）我们提出了一种多视图字典学习算法，以获得同时考虑所有视图线索的多视图图字典，从而提高 MIL 的判别能力。在许多实际应用中的实验和分析证明了该 MIL 的有效性。
