Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse




Journal Article

Analysis of Fixed-Point and Coordinate Descent Algorithms for Regularized Kernel Methods


Dinuzzo,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

Dinuzzo, F. (2011). Analysis of Fixed-Point and Coordinate Descent Algorithms for Regularized Kernel Methods. IEEE Transactions on Neural Networks, 22(10), 1576-1587. doi:10.1109/TNN.2011.2164096.

Cite as:
In this paper, we analyze the convergence of two general classes of optimization algorithms for regularized kernel methods with convex loss function and quadratic norm regularization. The first methodology is a new class of algorithms based on fixed-point iterations that are well-suited for a parallel implementation and can be used with any convex loss function. The second methodology is based on coordinate descent, and generalizes some techniques previously proposed for linear support vector machines. It exploits the structure of additively separable loss functions to compute solutions of line searches in closed form. The two methodologies are both very easy to implement. In this paper, we also show how to remove non-differentiability of the objective functional by exactly reformulating a convex regularization problem as an unconstrained differentiable stabilization problem.