hybrid_LDA metric learning package

Description

Implementation of hybrid metric learning algorithm described in:

B. Bue and E. Merényi, "An Adaptive Similarity Measure for
Classification of Hyperspectral Signatures," IEEE Geoscience and
Remote Sensing Letters, 2012.

Please cite the above reference if you publish work that uses this code.

Tested on:

• OSX 10.6
• Python 2.6, numpy 1.6, scipy 0.9
• Matlab R2011a

Example usage

To run the Python demo for several different lambda values:

   lambdas = [1e-5,1e-3,0,0.1,0.5,0.9]
   minDistHybrid([dat_ci,dat_cr],labels,lambdas,Dlab=["CI","CR"])

where:

• dat_ci and dat_cr: [N x d] arrays containing a set of N continuum-intact spectral signatures and their corresponding continuum-removed representations, respectively;
• labels: N-dimensional vector of class labels for each signature;
• lambdas: regularization values, each in the [0,1] range.

Functions to calculate the continuum-removed representation of a spectral signature are provided in the LINCR library available at:

https://github.com/dsmbgu8/LINCR

or

http://www.ece.rice.edu/~bdb1/#code.

Contact

Please contact the author (bbue@alumni.rice.edu) if you have any questions regarding this code.