Non-parametric Variable Selection on Non-linear Manifolds

Loann David Denis Desboulets (1)

1 AMSE - Aix-Marseille Sciences Economiques

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.

Mots clés: Non-parametric; Non-linear Manifolds; Variable Selection; Neural Networks

Domaines: Statistiques [stat]

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