Candidate Label-aware Similarity Graph for Partial Label Data
Tian Xie, Hongchang Chen, Chao Gao, Shaomei Li, Ruiyang Huang · 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2019
In partial label learning, the label of instance is represented by a label set, among which only one is the true label. The existing partial label learning algorithm only use features of instances to measure the similarity, and lacks of utilization of information hidden in the label set. In this paper, we proposes a candidate label-aware similarity graph constructing method for partial label data which effectively combines candidate label information using Jaccard distance and linear reconstruction to measure the similarity between instances. First, we calculate the similarity between instances and candidate label-sets. Then, the similarity graph is constructed by combining the instance similarity and the label-set similarity. Extensive experiments on artificial as well as real-world PL data sets show that our method can measure the similarity of partial-label data better and improve the performance of graph-based partial label learning algorithm obviously.