KLMNN: The kernelized version of LMNN.

In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description

The kernelized version of LMNN.

Usage

KLMNN(num_dims = NULL, learning_rate = "adaptive", eta0 = 0.3,
  initial_metric = NULL, max_iter = 100, prec = 1e-08, tol = 1e-08,
  k = 3, mu = 0.5, learn_inc = 1.01, learn_dec = 0.5,
  eta_thres = 1e-14, kernel = "linear", gamma = NULL, degree = 3,
  coef0 = 1, kernel_params = NULL, target_selection = "kernel")

Arguments

num_dims	Desired value for dimensionality reduction. Ignored if solver is 'SDP'. If NULL, all features will be kept. Integer.
learning_rate	Type of learning rate update for gradient descent. Possible values are: - 'adaptive' : the learning rate will increase if the gradient step is succesful, else it will decrease. - 'constant' : the learning rate will be constant during all the gradient steps.
eta0	The initial value for learning rate.
initial_metric	If array or matrix, and solver is SDP, it must be a positive semidefinite matrix with the starting metric (d x d) for gradient descent, where d is the number of features. If None, euclidean distance will be used. If a string, the following values are allowed: - 'euclidean' : the euclidean distance. - 'scale' : a diagonal matrix that normalizes each attribute according to its range will be used. If solver is 'SGD', then the array or matrix will represent a linear map (d' x d), where d' is the dimension provided in num_dims.
max_iter	Maximum number of iterations of gradient descent. Integer.
prec	Precision stop criterion (gradient norm). Float.
tol	Tolerance stop criterion (difference between two iterations). Float.
k	Number of target neighbors to take. If this algorithm is used for nearest neighbors classification, a good choice is to take k as the number of neighbors. Integer.
mu	The weight of the push error in the minimization algorithm. The objective function is composed of a push error, given by the impostors, with weight mu, and a pull error, given by the target neighbors, with weight (1-mu). It must be between 0.0 and 1.0.
learn_inc	Increase factor for learning rate. Ignored if learning_rate is not 'adaptive'. Float.
learn_dec	Decrease factor for learning rate. Ignored if learning_rate is not 'adaptive'. Float.
eta_thres	A learning rate threshold stop criterion. 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.
target_selection	How to find the target neighbors. Allowed values are: - 'kernel' : using the euclidean distance in the kernel space. - 'original' : using the euclidean distance in the original space.

Value

The KLMNN transformer, structured as a named list.

References

Kilian Q Weinberger and Lawrence K Saul. “Distance metric learning for large margin nearest neighbor classification”. In: Journal of Machine Learning Research 10.Feb (2009), pages 207-244.

Lorenzo Torresani and Kuang-chih Lee. “Large margin component analysis”. In: Advances in neural information processing systems. 2007, pages 1385-1392.

jlsuarezdiaz/rDML documentation built on May 24, 2019, 12:35 a.m.