Transductive optimal component analysis

We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations as an optimization one on the underlying nonlinear manifold. An additional term is used to prefer representations with large ldquomarginsrdquo when classifying unlabeled data in the nearest classifier sense, a generalization of transductive support vector machines to learning representations. Experimental results of the proposed algorithm on face recognition data sets show the potential significant improvement for classification accuracy on test sets.