DMLMJ

Supervised distance metric learning through maximization of the Jeffrey divergence

How to learn a linear transformation A ?

Prerequisites

This has been tested using MATLAB 2010A and later on Windows and Linux (Mac should be fine).

Installation

Download the folder "DMLMJ" into the directory of your choice. Then within MATLAB go to file >> Set path... and add the directory containing "DMLMJ" to the list (if it isn't already). That's it.

Usage

First we need to learn a linear transformation from supervised data

params = struct();
params.kernel = 0;
params.knn = 5;
params.k1 = 5;
params.k2 = 5;
params.dim = 10;
>> L = DMLMJ(XTr, YTr, params)

Parameters

• XTr: Training examples (d x n, where d is the number of features and n is the number of examples)
• YTr: Training labels (n x 1)
• params (optional):
  • .kernel (If set to 1, a kerned method is applied, default = 0)
  • .ker (Kernel type: 'rbf' or 'poly' will be applied, default = 'rbf')
  • .knn (Number of neighbors, default = 5)
  • .k1 (Positive neighbors)
  • .k2 (Negative neighbors)
  • .dim (Desired number of dimensionality, default = cross-validation)

Once we have learned L, we can use it for unsupervised data

>> X = L'*X;

Authors

• Bac Nguyen Cong

Acknowledgments

If you find this code useful in your research, please consider citing:

@Article{Nguyen2016,
  Title       = {Supervised distance metric learning through maximization of the {J}effrey divergence},
  Author      = {Bac Nguyen and Carlos Morell and De Baets, Bernard},
  Journal     = {Pattern Recognition},
  Year        = {2017},
  Pages       = {215-225},
  Volume      = {64}
}