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This paper presents a probabilistic kernel learning based Gaussian mixture distributions in medical images registration. Gaussian distributions lie on Riemannian manifold, where high dimensional data possesses rich geometry structures. However, nonlinear geometry of Riemannian manifold in linear space gives rise to inferior registration results. Accordingly, kernel method is used to embed a given...
Distribution calibration plays an important role in cross-domain learning. However, existing distribution distance metrics are not geodesic; therefore, they cannot measure the intrinsic distance between two distributions. In this paper, we calibrate two distributions by using the geodesic distance in Riemannian symmetric space. Our method learns a latent subspace in the reproducing kernel Hilbert...
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