Kyu-Hwan Jung

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In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed.(More)
We deal with the nonlinear manifold learning problem to find a low-dimensional structure in high-dimensional data. Based on Gaussian random fields framework, we propose an approximate sampling method for coordinates on the manifolds. Experimentally the mean of samples are shown to be almost equal to the coordinates obtained by locally linear embedding where(More)
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