Name
Abstract | ||
|---|---|---|
Kernel parameters optimization is one of the most challenging problems on kernel Fisher discriminant analysis (KFDA). In this paper, a simple and effective KFDA kernel parameters optimization criterion is proposed on the basis of the maximum margin criterion (MMC) that maximize the distances between any two classes. Actually, this MMC-based criterion is applied to the kernel parameters optimization on KFDA and KFDA with Locally Linear Embedding affinity matrix (KFDA-LLE). It is demonstrated by the experiments on six real-world multiclass datasets that, in comparison with two other criteria, our MMC-based criterion can detect the optimal KFDA kernel parameters more accurately in the cases of both RBF kernel and polynomial kernel. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-12436-0_37 | ADVANCES IN NEURAL NETWORKS - ISNN 2014 |
Keywords | Field | DocType |
Kernel parameter optimization,Maximum margin criterion,Feature extraction,Kernel Fisher discriminant analysis (KFDA),Affinity matrix | Affinity matrix,Kernel (linear algebra),Embedding,Pattern recognition,Radial basis function kernel,Kernel Fisher discriminant analysis,Feature extraction,Polynomial kernel,Artificial intelligence,Variable kernel density estimation,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
8866 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |