Semisupervised kernel orthonormalized partial least squares

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI database classification, Olivetti face database manifold learning, and in high-dimensional hyperspectral satellite image segmentation. High accuracy gains are obtained over KPLS in terms of expressive power of the extracted non-linear features. Matlab code is available at http://isp.uv.es for the interested readers.