LMNN: Large Margin Nearest Neighbors (LMNN).
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description Usage Arguments Value References

A distance metric learning algorithm that obtains a metric with target neighbors as near as possible and impostors as far as possible.

LMNN(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, soft_comp_interval = 1, learn_inc = 1.01,
  learn_dec = 0.5, eta_thres = 1e-14, solver = "SDP")

`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.
`soft_comp_interval`	Intervals of soft computation. The soft computation relaxes the gradient descent conditions, but makes the algorithm more efficient. This value provides the length of a soft computation interval. After soft_comp_interval iterations of gradient descent, a complete gradient step is performed. Integer.
`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.
`solver`	The algorithm used for minimization. Allowed values are: - 'SDP' : semidefinite programming, consisting of gradient descent with projections onto the positive semidefinite cone. It learns a metric. - 'SGD' : stochastic gradient descent. It learns a linear transformer.

The LMNN transformer, structured as a named list.

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.

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