—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance...
Seung-Jean Kim, Alessandro Magnani, Stephen P. Boy...
Abstract. By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coe...
Kitsuchart Pasupa, Robert F. Harrison, Peter Wille...
It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the Kernel Discriminant Analysis (KDA) alg...
Shuicheng Yan, Dong Xu, Lei Zhang, Benyu Zhang, Ho...
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denois...