Tractable Measure of Component Overlap for Gaussian Mixture Models

Abstract: The ability to quantify distinctness of a cluster structure is fundamental for certain simulation studies, in particular for those comparing performance of different classification algorithms. The intrinsic integral measure based on the overlap of corresponding mixture components is often analytically intractable. This is also the case for Gaussian mixture models with unequal covariance matrices when space dimension $d > 1$. In this work we focus on Gaussian mixture models and at the sample level we assume the class assignments to be known. We derive a measure of component overlap based on eigenvalues of a generalized eigenproblem that represents Fisher's discriminant task. We explain rationale behind it and present simulation results that show how well it can reflect the behavior of the integral measure in its linear approximation. The analyzed coefficient possesses the advantage of being analytically tractable and numerically computable even in complex setups.