BBAuthors: Zhang, C.; Advisor: -; Participants: Yu, Z.; Fu, H.; Zhu, P.; Chen, L.; Hu, Q. (2019)
For real-world applications, multilabel learning usually suffers from unsatisfactory training data. Typically, features
may be corrupted or class labels may be noisy or both. Ignoring noise in the learning process tends to result in an unreasonable model and, thus, inaccurate prediction. Most existing methods only consider either feature noise or label noise in multilabel learning. In this paper, we propose a unified robust multilabel learning framework for data with hybrid noise, that is, both feature noise and label noise. The proposed method,
hybrid noise-oriented multilabel learning (HNOML), is simple but rather robust for noisy data. HNOML simultaneously addresses feature and la... 
