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Kernel (or similarity) matrix plays a key role in many machine learning algorithms such as kernel methods, manifold learning, and dimension reduction. However, the cost of storing and manipulating the complete kernel matrix makes it infeasible for large problems. The Nyström method is a popular sampling-based low-rank approximation scheme for reducing the computational burdens in handling large kernel...
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical correlation with output targets. DRR is particularly suited for visualization of high dimensional data as well as the efficient regressor design with a reduced input dimension. In this paper we propose a novel nonlinear method...
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