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Non-parametric Variable Selection on Non-linear Manifolds

Abstract : In this paper, I investigate a new non-parametric variable selection framework. To extend the usual non-parametric model, I consider non-linear manifolds which are more flexible. Non-linear manifolds are represented by function compositions, allowing more complex dependences in the data. Based on two manifold approximation techniques , k-nearest neighbours and auto-encoder neural networks, I propose two different procedures to perform non-parametric variable selection. The two methods are complementary , the former being a local estimator, while the latter is a global estimator.
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https://hal-amu.archives-ouvertes.fr/hal-02986707
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Submitted on : Tuesday, November 3, 2020 - 11:11:33 AM
Last modification on : Wednesday, November 3, 2021 - 5:48:53 AM
Long-term archiving on: : Thursday, February 4, 2021 - 6:19:29 PM

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Loann Desboulets. Non-parametric Variable Selection on Non-linear Manifolds. 2020. ⟨hal-02986707⟩

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