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A Leave-K-Out Cross-Validation Scheme for Unsupervised Kernel Regression (2008)

Abstract
We show how to employ leave-K-out cross-validation in Unsupervised Kernel Regression, a recent method for learning of nonlinear manifolds. We thereby generalize an already present regularization method, yielding more flexibility without additional computational cost. We demonstrate our method on both toy and real data. 1

Publication details
Download http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.4056
Source http://www.techfak.uni-bielefeld.de/ags/ni/publications/media/KlankeRitter2006_LKO_draft.pdf
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Repository CiteSeerX - Scientific Literature Digital Library and Search Engine (United States)
Type text
Language English
Relation 10.1.1.111.3313, 10.1.1.21.1417, 10.1.1.131.3745, 10.1.1.130.110, 10.1.1.122.4566