Consistency of regularized sliced inverse regression for kernel models
نویسندگان
چکیده
We develop an extension of the sliced inverse regression (SIR) framework for dimension reduction using kernel models and Tikhonov regularization. The result is a numerically stable nonlinear dimension reduction method. We prove consistency of the method under weak conditions even when the reproducing kernel Hilbert space induced by the kernel is infinite dimensional. We illustrate the utility of this approach on simulated and real data.
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