Multi-label Learning With Highly Incomplete Data Via Collaborative Embedding

Authors:
Yufei Han Symantec Research Labs
Yun Shen Symantec Research Labs
Guolei Sun King Abdullah University of Science and Technology (KAUST
Xiangliang Zhang King Abdullah University of Science and Technology (KAUST

Introduction:

This paper studies improving the effectiveness of multi-label learning with incomplete label assignments. The authors propose a weakly supervised multi-label learning approach, based on the idea of collaborative embedding.

Abstract:

Tremendous 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.

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