Sorting out typicality with the inverse moment matrix SOS polynomial

Jean-Bernard Lasserre (1) , Edouard Pauwels (2)

1 LAAS-MAC - Équipe Méthodes et Algorithmes en Commande
2 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

We study a surprising phenomenon related to the representation of a cloud of data points using polynomials. We start with the previously unnoticed empirical observation that, given a collection (a cloud) of data points, the sublevel sets of a certain distinguished polynomial capture the shape of the cloud very accurately. This distinguished polynomial is a sum-of-squares (SOS) derived in a simple manner from the inverse of the empirical moment matrix. In fact, this SOS polynomial is directly related to orthogonal polynomials and the Christoffel function. This allows to generalize and interpret extremality properties of orthogonal polynomials and to provide a mathematical rationale for the observed phenomenon. Among diverse potential applications, we illustrate the relevance of our results on a network intrusion detection task for which we obtain performances similar to existing dedicated methods reported in the literature.

Domaines: Apprentissage [cs.LG]

Jean-Bernard Lasserre, Edouard Pauwels. Sorting out typicality with the inverse moment matrix SOS polynomial. 30th Conference on Neural Information Processing Systems (NIPS 2016), Dec 2016, Barcelone, Spain. pp.1-16, ⟨10.48550/arXiv.1606.03858⟩. ⟨hal-01331591⟩