Multi-instance Embedding Learning Through High-level Instance Selection

Multi-instance learning (MIL) handles complex structured data represented by bags and their instances. MIL embedded algorithms based on representative instance selection transform bags into a single-instance space. However, they may select weak representative instances due to the ignorance of the internal bag structure. In this paper, we propose the multi-instance embedding learning through high-level instance selection (MIHI) algorithm with two techniques. The fast bag-inside instance selection technique obtains instance prototypes of each bag. It fully utilizes the bag information using our new density and affinity metrics. Based on the instance prototypes, the high-level instance selection technique chooses instances using the peak density metric. It obtains high-level instances with higher representative power than the instance prototypes. Experiments were conducted on six learning tasks and nine comparison algorithms. The results confirmed that MIHI achieved better performance in terms of efficiency and classification accuracy. This method, in particular, has a substantial advantage in image retrieval and web data sets.