Semi-supervised learning by search of optimal target vector

We introduce a semi-supervised learning estimator which tends to the first kernel principal component as the number of labeled points vanishes. We show application of the proposed method for dimensionality reduction and develop a semi-supervised regression and classification algorithm for transductive inference.