Multi-label Learning with Highly Incomplete Data via Collaborative Embedding
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-07-19
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/628779
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AbstractTremendous efforts have been dedicated to improving the effectiveness of multi-label learning with incomplete label assignments. Most of the current techniques assume that the input features of data instances are complete. Nevertheless, the co-occurrence of highly incomplete features and weak label assignments is a challenging and widely perceived issue in real-world multi-label learning applications due to a number of practical reasons including incomplete data collection, moderate labels from annotators, etc. Existing multi-label learning algorithms are not directly applicable when the observed features are highly incomplete. In this work, we attack this problem by proposing a weakly supervised multi-label learning approach, based on the idea of collaborative embedding. This approach provides a flexible framework to conduct efficient multi-label classification at both transductive and inductive mode by coupling the process of reconstructing missing features and weak label assignments in a joint optimisation framework. It is designed to collaboratively recover feature and label information, and extract the predictive association between the feature profile and the multi-label tag of the same data instance. Substantial experiments on public benchmark datasets and real security event data validate that our proposed method can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.
CitationHan Y, Sun G, Shen Y, Zhang X (2018) Multi-label Learning with Highly Incomplete Data via Collaborative Embedding. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD ’18. Available: http://dx.doi.org/10.1145/3219819.3220038.
SponsorsThis work is partially supported by King Abdullah University of Science and Technology (KAUST).
JournalProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18
Conference/Event name24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018