1. A method for training a transductive support vector machine, based on a non-convex transductive support vector machine objective function, the transductive support vector machine objective function based on two ramp loss functions for unlabeled test data, the method comprising: decomposing each of the ramp loss functions into a difference of hinge losses, with a computer processor; using the differences of the hinge losses to decompose the transductive support vector machine objective function into a sum of a concave function and a convex function, with a computer processor; and determining a hyperplane classifier classifying the unlabeled data into first and second classes with a computer processor by iteratively approximating the concave function.