Multiple kernel discriminant analysis

This paper proposes a multiple kernel construction method for kernel discriminant analysis. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization. The experiments on several UCI real data benchmarks show that, the constructed kernel with optimized weights results in high classification accuracy, compared with multiple kernel learning under the framework of support vector machines. The experiments also show that the constructed kernel relaxes parameter selection for kernel discriminant analysis to some extent.