Description Usage Arguments Value References
Distance Metric Learning through the likelihood maximization of a logistic based probability distribution.
1 2 3 4 |
num_dims |
Number of dimensions for dimensionality reduction. Not supported yet. |
b |
Logistic function positive threshold. |
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'. |
The MCML transformer, structured as a named list.
Matthieu Guillaumin, Jakob Verbeek and Cordelia Schmid. “Is that you? Metric learning approaches for face identification”. In: Computer Vision, 2009 IEEE 12th international conference on. IEEE. 2009, pages 498-505.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.