Meta Multi-Instance Multi-Label learning by heterogeneous network fusion

Abstract

Multi-Instance Multi-Label Learning (MIML) models complex objects (bags), each of which is composed with a set of instances and associated with a set of labels. Current MIML solutions still focus on a single-type of bags and assume an independent and identically distribution (IID) of training data. But these bags are linked with objects of other types, which also encode the semantics of bags. In addition, they generally need abundant labeled data for training. To effectively mine MIML objects linked with objects of other types, we propose a heterogeneous network embedding and meta learning based approach (MetaMIML). MetaMIML introduces the context learner with network embedding to learn context representations of bags for structure information extraction, the task learner to extract the meta knowledge for fast adapting to new tasks with scarce training objects, and finally fuses the structural and attribute information to predict the labels of bags/instances. In this way, MetaMIML can not only naturally deal with MIML objects at data level improving, but also exploit the power of meta-learning at the model enhancing. Experiments on benchmark datasets demonstrate that MetaMIML achieves a significantly better performance than state-of-the-art algorithms.