Protein function prediction using weak-label learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Image annotation using metric learning in semantic neighbourhoods
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Social tag enrichment via automatic abstract tag refinement
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Image annotation with weak labels
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
Automatic Abstract Tag Detection for Social Image Tag Refinement and Enrichment
Journal of Signal Processing Systems
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We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c_1, c_2, c_3) is only assigned to class c_1 when it is used as a training sample. We refer to this problem as multi-label learning with incomplete class assignment. Incompletely labeled data is frequently encountered when the number of classes is very large (hundreds as in MIR Flickr dataset) or when there is a large ambiguity between classes (e.g., jet vs plane). In both cases, it is difficult for users to provide complete class assignments for objects. We propose a ranking based multi-label learning framework that explicitly addresses the challenge of learning from incompletely labeled data by exploiting the group lasso technique to combine the ranking errors. We present a learning algorithm that is empirically shown to be efficient for solving the related optimization problem. Our empirical study shows that the proposed framework is more effective than the state-of-the-art algorithms for multi-label learning in dealing with incompletely labeled data.