Large scale semi-supervised learning using KSC based model

Often in practice one deals with a large amount of unlabeled data, while the fraction of labeled data points will typically be small. Therefore one prefers to apply a semi-supervised algorithm, which uses both labeled and unlabeled data points in the learning process, to have a better performance. Considering the large amount of unlabeled data, making a semi-supervised algorithm scalable is an important task. In this paper we adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it scalable by means of two different approaches. The first one is based on the Nystrom approximation method which provides a finite dimensional feature map that can then be used to solve the optimization problem in the primal. The second approach is based on the reduced kernel technique that solves the problem in the dual by reducing the dimensionality of the kernel matrix to a rectangular kernel. Experimental results demonstrate the scalability and efficiency of the proposed approaches on real datasets.