Multiple-kernel One-class Multiple-instance Learning Algorithm

By introducing multiple-kernel into one-class multiple-instance learning, this paper proposes a novel multiple-kernel one-class multiple-instance learning based on support vector data description, which aims to solve the problem of multiple-kernel learning of multiple-instance data with a relatively complex distribution structure in practical applications. This algorithm maps multiple-instance data into the feature space through different multiple-kernel functions, and constructs a spherical classifier by using support vector data description algorithm. To iteratively optimize the proposed algorithm adopts an iterative optimization framework, we first initialize the instances in positive bags as positive, and optimize the objective function to build up the classifier. Then, the labels of the positive instances in each bag are updated according to the classifier obtained in the previous step. The experimental results on the Corel, VOC 2007 and Messidor datasets show that the proposed algorithm achieves significantly better classification performance than state-of-the-art methods, demonstrating its feasibility and effectiveness.