In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multiple-instance learning as a combinatorial maximum margin optimization problem with additional instance selection constraints within the framework of support vector machines. Although solving this primal problem requires non-convex programming, we nevertheless can then derive an equivalent dual formulation that can be relaxed into a novel convex semidefinite programming (SDP). The relaxed SDP has free parameters where T is the number of instances, and can be solved using a standard interior-point method. Empirical study shows promising performance of the proposed SDP in comparison with the support vector machine approaches with heuristic optimization procedures.

Additional Metadata
Persistent URL dx.doi.org/10.1007/978-3-642-05224-8_9
Series Lecture Notes in Computer Science
Citation
Guo, Y. (2009). Max-margin Multiple-Instance Learning via Semidefinite Programming. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-05224-8_9