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Preprints, Working Papers, ... Year : 2020

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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Dates and versions

hal-02986707 , version 1 (03-11-2020)

Identifiers

  • HAL Id : hal-02986707 , version 1

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