Description Usage Arguments Value References
The kernelized version of DMLMJ.
1 2 3 |
num_dims |
Dimension desired for the transformed data. If NULL, dimension will be the number of features. |
n_neighbors |
Number of neighbors to consider in the computation of the difference spaces. |
alpha |
Regularization parameter for inverse matrix computation. |
reg_tol |
Tolerance threshold for applying regularization. The tolerance is compared with the matrix determinant. |
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 KDMLMJ transformer, structured as a named list.
Bac Nguyen, Carlos Morell and Bernard De Baets. “Supervised distance metric learning through maximization of the Jeffrey divergence”. In: Pattern Recognition 64 (2017), pages 215-225.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.