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Kernel ridge regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter sigma. We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian process. Since...
A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal...
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