###This is a test, thanks to the CS department of Cornell, and all the files are downloaded from https://www.cs.cornell.edu/~kilian/code/code.html

(Thanks to John Blitzer, who gave me this cake for my 30th birthday.)

This is a MATLAB implementation of Large Margin Nearest Neighbor (LMNN), a metric learning algorithm first introduced by Kilian Q. Weinberger, John C. Blitzer and Lawrence K. Saul in 2005. LMNN is a metric learning algorithm to improve k-nearest neighbor classification by learning a generalized Euclidean metric especially for nearest neighbor classification. For more details on the solver see the 2009 JMLR paper.

(The current version is 3.0.1.)

To see a working demo, please run (inside the MATLAB console):

Version 3: setpaths3 cd demos isoletdemo

Version 2: install demo

If you use this code in scientific work, please cite:

#!bibtex
@article{weinberger2009distance,
  title={Distance metric learning for large margin nearest neighbor classification},
  author={Weinberger, K.Q. and Saul, L.K.},
  journal={The Journal of Machine Learning Research},
  volume={10},
  pages={207--244},
  year={2009},
  publisher={JMLR.org}
}

• update 04/08/2015
  • added simple implementation of Neighbourhood Component Analysis (NCA)
• update 03/31/2015
  • released version 3.0.0
  • Version 3 uses the squared LMNN loss (which is differentiable) and the optimization is usesMark Schmidt's LBFGS implementation
• update 03/27/2015
  • released version 2.6.0
  • this version has a much simpler, clearer and faster gradient computation
  • other small bug fixes
• update 02/26/2015
  • fixed a bug in SOD.m (for very large data sets)
  • added a simple bandit algorithm to choose between SOD.m and SODmex.c during runtime
  • Added sd2b.c, which speeds up gradient computation for smallish data sets.
  • Added findLMNNparams.m for automatic tune LMNN parameters
• update 01/23/2015
  • Release version 2.5.1
  • moved to Bitbucket as new host
  • Fixed bug
• update 23/04/2014
  • Release version 2.5:
  • introduce new parameter "subsample" (subsample 10% of constraints by default)
  • improve convergence criteria
• update 10/04/2013
  • fixed a small but critical bug in applypca.m (this function is optional as pre-processing)
• update 09/17/2013 -Version 2.4.1:
  • Set default validation parameter to 0.2
  • Now perform cross validation over maxstepsize automatically
• update 07/26/2013
  • Version 2.4:
  • Added GB-LMNN
  • New demo.m (now including GB-LMNN)
  • Made small changes to LMNN (mostly usability)
  • Parallelized some C-functions with open-MP
  • Thanks to Gao Huang (Tsinghua university) for helping with the GB-LMNN implementation
• update 13/11/2012
  • Fixed a bug that prevented execution with even values for k.
• update 01/11/2012
  • Added optional 'diagonal' version to learn diagonal matrices
• update 09/19/2012
  • Added 32-bit Windows binaries (Thanks to Ya Shi)
• update 09/18/2012
  • Added parameter 'outdim' to easily specify the output dimensionality
  • Small fixes in mtree code, which broke compilation on some windows machines.
  • Speedup in findimps3Dm by substituting some repmats with bsxfun (somehow they have been overlooked)
• update 09/13/2012
  • Small fix to setpaths.m script
  • Rearranged files to ensure that the mexed files are in the path.
  • updated demo
• update 09/06/2012
  • Small fix to install.m script
• update 08/23/2012
  • Removed mex files which are no longer faster than the Matlab equivalent (Matlab became a lot faster over the years)
  • Updated mtrees to compile on windows computers and no longer use depreciated libraries
  • Removed all BLAS / LAPACK dependencies
  • Renamed knnclassify.m to knncl.m (as former clashed with the implementation from the statistics toolbox)
  • (Many thanks to Jake Gardner who helped a lot with tiding up of the code.)

(LMNN is different from the work by Domeniconi et al. 2005 with a very similar title.)