Data analysis from empirical moments and the Christoffel function
This work addresses data analysis challenges in machine learning for datasets with complex structures, offering a refined tool for practitioners, though it appears incremental in building on existing knowledge of moment matrices.
The paper tackles the problem of analyzing data supported on singular sets by leveraging the empirical moment matrix, combining insights from statistics, real algebraic geometry, orthogonal polynomials, and approximation theory. It provides theoretical support, numerical experiments, and connections to real-world data to validate this approach.
Spectral features of the empirical moment matrix constitute a resourceful tool for unveiling properties of a cloud of points, among which, density, support and latent structures. It is already well known that the empirical moment matrix encodes a great deal of subtle attributes of the underlying measure. Starting from this object as base of observations we combine ideas from statistics, real algebraic geometry, orthogonal polynomials and approximation theory for opening new insights relevant for Machine Learning (ML) problems with data supported on singular sets. Refined concepts and results from real algebraic geometry and approximation theory are empowering a simple tool (the empirical moment matrix) for the task of solving non-trivial questions in data analysis. We provide (1) theoretical support, (2) numerical experiments and, (3) connections to real world data as a validation of the stamina of the empirical moment matrix approach.