The kernelized version of ANMM.
KANMM(num_dims = NULL, n_friends = 3, n_enemies = 1, kernel = "linear", gamma = NULL, degree = 3, coef0 = 1, kernel_params = NULL)
num_dims | Dimension desired for the transformed data. Integer. If NULL, all features will be taken. Integer. |
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n_friends | Number of nearest same-class neighbors to compute homogeneus neighborhood. Integer. |
n_enemies | Number of nearest different-class neighbors to compute heterogeneus neigborhood. Integer. |
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 KANMM transformer, structured as a named list.
Fei Wang and Changshui Zhang. “Feature extraction by maximizing the average neighborhood margin”. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on. IEEE. 2007, pages 1-8.