MAP Clustering under the Gaussian Mixture Model via Mixed Integer Nonlinear Optimization

no code implementations • 8 Nov 2019 • Patrick Flaherty, Pitchaya Wiratchotisatian, Ji Ah Lee, Zhou Tang, Andrew C. Trapp

We present a global optimization approach for solving the maximum a-posteriori (MAP) clustering problem under the Gaussian mixture model. Our approach can accommodate side constraints and it preserves the combinatorial structure of the MAP clustering problem by formulating it asa mixed-integer nonlinear optimization problem (MINLP).

Clustering