80 citations · 87 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Learning from Positive and Unlabeled Data with Augmented Classes
Zhongnian Li, Liutao Yang, Zhongchen Ma +3
Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learn…
cs.LG2022★ 7 cited
A Similarity-based Framework for Classification Task
Zhongchen Ma, Songcan Chen
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting i…
cs.LG2020★ 80 cited
Expand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels
Zhongchen Ma, Songcan Chen
In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. How…