[KDD 2020] Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism
Aug 13, 20209 views
Partial Multi-Label learning (PML) learns from the ambiguous data,where each instance is associated with a candidate label set, where,only a part is correct. The key to solve such problem is to disambiguate the candidate label sets and identify the correct assignments,between instances and their ground-truth labels. In this paper, we,interpret such assignments as,instance-to-label,matchings, and formulate the task of PML as a matching selection problem. To model,such problem, we propose a novel grap,H,m,A,tching based partial,mu,L,ti-label l,E,arning (,HALE,) framework, where,Graph Matching,scheme is incorporated owing to its good performance of exploiting,the instance and label relationship. Meanwhile, since conventional,one-to-one,graph matching algorithm does not satisfy the constraint,of PML problem that multiple instances may correspond to multiple,labels, we extend the traditional probabilistic graph matching algorithm from,one-to-one,constraint to,many-to-many,constraint, and,make the proposed framework to accommodate to the PML problem. Moreover, to improve the performance of predictive model,,both the minimum error reconstruction and,k,-nearest-neighbor,weight voting scheme are employed to assign more accurate labels,for unseen instances. Extensive experiments on various data sets,demonstrate the superiority of our proposed method.