MCML: Maximally Collapsing Metric Learning (MCML).
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description Usage Arguments Value References

A distance metric learning algorithm that learns minimizing the KL divergence to the maximally collapsing distribution.

MCML(num_dims = NULL, learning_rate = "adaptive", eta0 = 0.01,
  initial_metric = NULL, max_iter = 20, prec = 0.01, tol = 0.01,
  descent_method = "SDP", eta_thres = 1e-14, learn_inc = 1.01,
  learn_dec = 0.5)

`num_dims`	Number of dimensions for dimensionality reduction. Not supported yet.
`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. Float.
`initial_metric`	If array or matrix, it must be a positive semidefinite matrix with the starting metric 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.
`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: - 'SDP' : semidefinite programming, consisting of gradient descent with projections onto the PSD cone.
`eta_thres`	A learning rate threshold stop criterion.
`learn_inc`	Increase factor for learning rate. Ignored if learning_rate is not 'adaptive'.
`learn_dec`	Decrease factor for learning rate. Ignored if learning_rate is not 'adaptive'.