Discriminant analysis in high dimensionality using the kernel trick.
KDA(solver = "eigen", n_components = NULL, tol = 1e-04, kernel = "linear", gamma = NULL, degree = 3, coef0 = 1, kernel_params = NULL)
solver | Solver to use, posible values: - 'eigen': Eigenvalue decomposition. |
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n_components | Number of components (lower than number of classes -1) for dimensionality reduction. If NULL, classes - 1 is used. Integer. |
tol | Singularity toleration level. Float. |
kernel | Kernel to use. Allowed values are: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed". |
gamma | Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels. Default value is 1/n_features. Float. |
degree | Degree for poly kernels. Ignored by other kernels. Integer. |
coef0 | Independent term for poly and sigmoid kernels. Ignored by other kernels. Float. |
kernel_params | Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels. |
The KDA transformer, structured as a named list.
Sebastian Mika et al. “Fisher discriminant analysis with kernels”. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop. Ieee. 1999, pages 41-48.