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ITML: Information Theoretic Metric Learning

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Latest version: 1.2

About ITML

ITML is a Matlab implementation of Information Theoretic Metric Learning algorithm. Metric learning involves finding a suitable metric for a given set of data-points with side-information regarding distances between few datapoints. ITML characterizes the metric using a Mahalanobis distance function and learns the associated parameters using Bregman's cyclic projection algorithm.

This software is currently being actively maintained; please check back often for updates. When using this code, please cite ITML and the relevant paper.

@inproceedings{davis-et-al-icml-2007,
  author    = {Jason V. Davis and
               Brian Kulis and
               Prateek Jain and
               Suvrit Sra and
               Inderjit S. Dhillon},
  title     = {Information-theoretic metric learning},
  booktitle = {ICML},
  year      = {2007},
  pages     = {209-216},
  address   = {Corvalis, Oregon, USA}
  month     = {June}
}

@manual{itml,
  title        = {Information Theoretic Metric Learning},
  author       = {Jason V. Davis and Brian Kulis and Prateek Jain and Suvrit Sra and Inderjit S. Dhillon},
  organization = {UT, Austin},
  address      = {http://www.cs.utexas.edu/users/pjain/itml/},
}

Downloads

Source code

Download the latest version (1.2, released 2008-4-28): itml-1.2.tar.gz

Download version 1.1, released 2008-2-11): itml-1.1.tar.gz

Example code and data


Documentation

README: How to install and use ITML.

Contact

If you have any questions, suggestions, or bug reports about this implementation, please contact Brian J. Kulis (kulis at cs dot utexas dot edu) and Prateek Jain (pjain at cs dot utexas dot edu).