Mode-finding for mixtures of Gaussian distributions
Miguel Á. Carreira-Perpiñán · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2000
Gradient-quadratic and fixed-point iteration algorithms and appropriate values for their control parameters are derived for finding all modes of a Gaussian mixture, a problem with applications in clustering and regression. The significance of the modes found is quantified locally by Hessian-based error bars and globally by the entropy as sparseness measure.