/conditional-entropy-metric-learning

Code to reproduce experiements from "Information Theoretic Learning Using Infinitely Divisible Kernels"

Primary LanguageMATLAB

Metric learning using matrix-based conditional entropy.
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This set of functions reproduces the results of the UCI experiments in the paper:
"Luis G Sanchez Giraldo and Jose Principe, Information Theoretic Learning Using 
Infinitely Divisible Kernels. ICLR 2013."

To run the experiments you must:

-- Uncompress "Metric_Learning.tar.gz" to your desired working directory.
-- Download the data.tar.gz from PERSONAL WEBPAGE uncompress in a desired location.
-- Modify the "data_path" variable with the location of the data in your machine.
-- Experiments reported in the above paper can be run by calling: 
   -- "UCI_comparisons.m" 
   -- "CEML_UMIS_faces.m"
   -- "CEML_alpha_comparison.m"

 
The function "CondEntropyMetricLearning.m" is a matlab implementation of the metric 
learning algorithm described in the paper.   


Implementations of NCA, ITML, LMNN, and MCML are part of the Matlab Toolbox for 
Dimensionality Reduction by 
	       "Laurens van der Maaten, Delft University of Technology"
This toolbox can be obtained from http://homepage.tudelft.nl/19j49

You are free to use, change, or redistribute this code in any way you want for 
non-commercial purposes. However, it is appreciated if you maintain the name of the 
original author.
(C) Luis Gonzalo Sanchez Giraldo, 2014