Kernel-Based Subspace Learning on Riemannian Manifolds for Visual Recognition
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DOI: 10.1007/s11063-019-10083-z
Abstract: Covariance matrices have attracted increasing attention for data representation in many computer vision tasks. The nonsingular covariance matrices are regarded as points on Riemannian manifolds rather than Euclidean space. A common technique for classification on… read more here.
Keywords: rkhs; space; kernel based; euclidean space ... See more keywords