Kernel classifier with Correntropy loss
Classification can be seen as a mapping problem where some function of xn predicts the expectation of a class variable yn. This paper uses kernel methods for the prediction of class variable, together with a recently proposed cost function for classification, called Correntropy-loss (C-loss) function. C-Loss is a non-convex loss function based on a similarity measure called correntropy and is known to closely approximate the ideal 0-1 loss function for classification. This paper shows via experimental results that, by replacing the cost function - Mean Square Error (MSE) in a conventional kernel based functional mapping, by a non-convex loss function C-Loss, a non-overfitting, and hence, a better classifier can be obtained. Since gradient descent can still be used with the C-loss and the kernel mapper, the classifier can be easily trained without performance penalty, compared to the SVM, which makes the approach very practical.