Abstract:Reproducing Kernel Hilbert Space (RKHS) subspace learning is very popular among the domain adaption, which learns a latent RKHS subspace for the source domain and target domain, so that their distribution gap becomes smaller than in the original data space. There is a famous probability theory: two second-order moment random variables are equal if and only if their mean squared error (MSE) is zero. In this paper, firstly, we use second-order moment random variables to model the source domain and target domain.… Show more
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