We give several properties of the reproducing kernel Hilbert space induced by the Gaussian kernel, along with their implications for recent results in the complexity of the regularized least square… Expand

We present a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting.Expand

We propose a general classification framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task.Expand

We propose a semi-supervised multi-class learning approach for person re-identification in the presence of a camera network with non-overlapped fields of view.Expand

We consider the general problem of learning an unknown functional dependency, f : X ↦ Y , between a structured input space X and a structured output space Y, from labeled and unlabeled examples.Expand

We present a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space.Expand

This paper introduces a novel mathematical and computational framework, namely Log-Hilbert-Schmidt metric between positive definite operators on a Hilbert space, for which we obtain explicit formulas via Gram matrices.Expand