PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
matthias seeger
Journal of Machine Learning Research 2007.

Abstract

We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, we focus on a Newton optimization of the complete model, making use of model structure and linear conjugate gradients in order to approximate Newton search directions. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels, and focusing code optimization efforts to these primitives only. Kernel parameters are learned automatically, by maximizing the cross-validation log likelihood in a gradient-based way, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical structure on throusands of classes, achieving state-of-the-art results in an order of magnitude less time than previous work.

EPrint Type:Article
Additional Information:Available at http://www.kyb.tuebingen.mpg.de/bs/people/seeger/
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3102
Deposited By:matthias seeger
Deposited On:19 December 2007