Sparse multiple instance learning with non-convex penalty

Abstract

Multiple instance learning (MIL) usually plays an important role in weakly labeled learning. It is able to reduce cost noted by accurate annotations and remove noisy samples with useless labels in large scale databases. However, in many real applications, the training data always contains many redundant features. In order to remove those residual features and improve the computational efficiency, some convex and nonconvex regularization terms are applied. In this paper we apply the MCP function to propose an efficient sparse modle SyMIL, and then use the proximal stochastic subgradient method (PSSG) to solve it. The convergence analysis of the algorithm is given in details. Extensive experiments show that our model can reduce redundant features effectively while ensuring the accuracy. Quantitative comparisons against several regularization terms demonstrate the advantages of the MCP regularizer in this model. Moreover, PSSG can be observed to have slightly faster convergence rate than some other methods.