NCA: Neighborhood Component Analysis (NCA).
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description Usage Arguments Value References

A distance metric learning algorithm that tries to minimize kNN expected error.

NCA(num_dims = NULL, learning_rate = "adaptive", eta0 = 0.3,
  initial_transform = NULL, max_iter = 100, prec = 1e-08,
  tol = 1e-08, descent_method = "SGD", eta_thres = 1e-14,
  learn_inc = 1.01, learn_dec = 0.5)

`num_dims`	Desired value for dimensionality reduction. If None, the dimension of transformed data will be the same as the original. 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_transform`	If array or matrix that will represent the starting linear map for gradient descent, where d is the number of features, and d' is the dimension specified in num_dims. 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.
`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.
`descent_method`	The descent method to use. Allowed values are: - 'SGD' : stochastic gradient descent. - 'BGD' : batch gradient descent.
`eta_thres`	A learning rate threshold stop criterion. Float.
`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.