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Poster
Sorting out typicality with the inverse moment matrix SOS polynomial
Edouard Pauwels · Jean Lasserre

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #68
in Monday Posters »

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.

Keywords: (Other) Unsupervised Learning Methods · Convex Optimization · Similarity and Distance Learning

Author Information

Edouard Pauwels (IRIT)
Jean Lasserre (LAAS-CNRS)

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