Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
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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 $\mathcal{O}(T)$ 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.