Sparsity Based Feature Extraction for Kernel Minimum Squared Error

Kernel minimum squared error(KMSE) is well-known for its effectiveness and simplicity, yet it suffers from the drawback of efficiency when the size of training examples is large. Besides, most of the previous fast algorithms based on KMSE only consider classification problems with balanced data, when in real world imbalanced data are common. In this paper, we propose a weighted model based on sparsity for feature selection in kernel minimum squared error(KMSE). With our model, the computational burden of feature extraction is largely alleviated. Moreover, this model can cope with the class imbalance problem. Experimental results conducted on several benchmark datasets indicate the effectivity and efficiency of our method.