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MM (majorization--minimization) algorithms are an increasingly popular toolfor solving optimization problems in machine learning and statisticalestimation. This article introduces the MM algorithm framework in general andvia three popular example applications: Gaussian mixture regressions,multinomial logistic regressions, and support vector machines. Specificalgorithms for the three examples are derived and numerical demonstrations arepresented. Theoretical and practical aspects of MM algorithm design arediscussed.

An Introduction to MM Algorithms for Machine Learning and Statistical