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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 positivedoi:10.1109/mlsp.2012.6349718 dblp:conf/mlsp/Izquierdo-VerdiguierAMGC12 fatcat:dcrpw456g5aybkh6krg5esyt3e