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.

• Type: Journal
• Year: 2008
• Where: PRL
• Authors: Leonardo Angelini, Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia
• Keywords: Kernel Principal Component, PRL 2008, Semi-supervised Learning Estimator, Semi-supervised Regression