Multi-label Learning with Incomplete Class Assignments

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Multi-label Learning with Incomplete Class Assignments
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 (c1, c2, c3) is only assigned to class c1 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 pro...
Serhat Bucak, Rong Jin, Anil Jain
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where CVPR
Authors Serhat Bucak, Rong Jin, Anil Jain
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