PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Multiple Kernels by Augmenting the Kernel Matrix
Fei Yan, Krystian Mikolajczyk, Josef Kittler and Muhammad Tahir
In: International Workshop on Multiple Classifier Systems 2010, 7-9 Apr 2010, Cairo, Egypt.

Abstract

In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional methods that learn a linear combination of n kernels of size m x m, resulting in m coefficients in the trained classifier, we propose a method that can learn n x m coefficients. This allows to assign different importance to the information channel per example rather than per kernel. We analyse the proposed kernel combination in empirical feature space and provide its geometrical interpretation. We validate the approach on both UCI datasets and an object recognition dataset, and demonstrate that it leads to classification improvements.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Multimodal Integration
ID Code:6215
Deposited By:Fei Yan
Deposited On:08 March 2010