A majorization-minimization algorithm for (multiple) hyperparameter learning

We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integrating out the hyperparameters from the model. We find a local optimum of the resulting non-convex optimization problem efficiently using a majorization-minimization (MM) algorithm, in which the non-convex problem is reduced to a series of convex L2regularized parameter estimation tasks. The principal appeal of our method is its simplicity: the updates for choosing the L2regularized subproblems in each step are trivial to implement (or even perform by hand), and each subproblem can be efficiently solved by adapting existing solvers. Empirical results on a variety of supervised learning models show that our algorithm is competitive with both grid-search and gradient-based algorithms, but is more efficient and far easier to implement. Authors: Chuan Sheng Foo, Chuong Do, Andrew Y. Ng (2009)
AUTHORED BY
Chuan Sheng Foo
Chuong Do
Andrew Y. Ng

Abstract

We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integrating out the hyperparameters from the model. We find a local optimum of the resulting non-convex optimization problem efficiently using a majorization-minimization (MM) algorithm, in which the non-convex problem is reduced to a series of convex L2regularized parameter estimation tasks. The principal appeal of our method is its simplicity: the updates for choosing the L2regularized subproblems in each step are trivial to implement (or even perform by hand), and each subproblem can be efficiently solved by adapting existing solvers. Empirical results on a variety of supervised learning models show that our algorithm is competitive with both grid-search and gradient-based algorithms, but is more efficient and far easier to implement.

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