LSI: Learning with Side Information (LSI).
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R

Description Usage Arguments Value References

A distance metric learning algorithm that minimizes the sum of distances between similar data, with non similar data constrained to be separated.

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LSI(initial_metric = NULL, learning_rate = "adaptive", eta0 = 0.1,
  max_iter = 100, max_proj_iter = 5000, itproj_err = 0.001,
  err = 0.001, supervised = FALSE)

`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.
`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.
`max_iter`	Number of iterations for gradient descent. Integer.
`max_proj_iter`	Number of iterations for iterated projections. Integer.
`itproj_err`	Convergence error criterion for iterated projections. Float.
`err`	Convergence error stop criterion for gradient descent.
`supervised`	If True, the algorithm will accept a labeled dataset (X,y). Else, it will accept the dataset and the similarity sets, (X,S,D).
`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.

The LSI transformer, structured as a named list.

Eric P Xing et al. “Distance metric learning with application to clustering with side-information”. In: Advances in neural information processing systems. 2003, pages 521-528.

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