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- W2911273279 abstract "We consider an unknown smooth function which maps high-dimensional inputs to multidimensional outputs and whose domain of definition is an unknown low-dimensional input manifold embedded in an ambient high-dimensional input space. Given a training dataset with input-output pairs, Regression with Manifold Valued Inputs problem is to estimate the unknown function and its Jacobian matrix. Previously proposed solutions are very computationally expensive. The paper presents a new geometrically motivated kernel regression method for solving the considered problem with a much lower computational complexity while preserving accuracy." @default.
- W2911273279 created "2019-02-21" @default.
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- W2911273279 date "2018-10-01" @default.
- W2911273279 modified "2023-09-27" @default.
- W2911273279 title "Kernel Regression on Manifold Valued Data" @default.
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- W2911273279 doi "https://doi.org/10.1109/dsaa.2018.00022" @default.
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