Dictionary-based multi-instance learning method with universum information

Fan Cao, Bo Liu, Kai Wang, Yanshan Xiao, Jinghui He, Jian Xu

Abstract: Multi-instance learning (MIL) is a generalized form of supervised learning that attempts to extract useful information from sets of instances, known as bags. In practice, besides positive and negative bags, we can also collect universum bags. These universum bags contain instances that do not fit within the defined positive or negative classes; they may belong to a third class or other categories, similar to universum instances in classical machine learning. Additionally, dictionary learning can be used to eliminate noise hidden in the data and enhance the performance of the learning tool. In this paper, we propose a new dictionary-based multi-instance learning method with universum data (UDMIL). In the proposed model, universum bags are considered prior knowledge within the training data for classifier construction. We construct three types of dictionaries for positive bags, negative bags, and universum bags to enhance the sparsity of the training data and develop a better classifier. In addition, we introduce an alternative learning framework to solve the proposed model and acquire the MIL classifier for prediction. Extensive experiments show that the proposed method achieves superior performance.