Learning Instance Specific Distance for Multi-Instance Classification


  • Hua Wang University of Texas at Arlington
  • Feiping Nie University of Texas at Arlington
  • Heng Huang University of Texas at Arlington


Multi-Instance Learning (MIL) deals with problems where each training example is a bag, and each bag contains a set of instances. Multi-instance representation is useful in many real world applications, because it is able to capture more structural information than traditional flat single-instance representation. However, it also brings new challenges. Specifically, the distance between data objects in MIL is a set-to-set distance, which is harder to estimate than vector distances used in single-instance data. Moreover, because in MIL labels are assigned to bags instead of instances, although a bag belongs to a class, some, or even most, of its instances may not be truly related to the class. In order to address these difficulties, in this paper we propose a novel Instance Specific Distance (ISD) method for MIL, which computes the Class-to-Bag (C2B) distance by further considering the relevances of training instances with respect to their labeled classes. Taking into account the outliers caused by the weak label association in MIL, we learn ISD by solving an l0+-norm minimization problem. An efficient algorithm to solve the optimization problem is presented, together with the rigorous proof of its convergence. The promising results on five benchmark multi-instance data sets and two real world multi-instance applications validate the effectiveness of the proposed method.




How to Cite

Wang, H., Nie, F., & Huang, H. (2011). Learning Instance Specific Distance for Multi-Instance Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 507-512. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7893



AAAI Technical Track: Machine Learning