.

this might be of interest ; not exactly "recent", though

http://www.cs.rug.nl/~biehl/Preprints/esann07matrix.pdf‎

Relevance Matrices in LVQ

Petra Schneider1, Michael Biehl1 and Barbara Hammer2

1- University of Groningen - Mathematics and Computing Science

P.O. Box 800, 9700 AV Groningen - The Netherlands

2- Clausthal University of Technology - Institute of Computer Science

Julius Albert Strasse 4, 38678 Clausthal-Zellerfeld - Germany

Abstract. We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account. In comparison to the weighted euclidean metric used for GRLVQ, this metric is more powerful to represent the internal structure of the data appropriately while maintaining its excellent generalization ability as large margin optimizer. The algorithm is tested and compared to alternative LVQ schemes using an artificial dataset and the image segmentation data from the UCI repository.

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I bearly read it :)

Hi Eren,

here are some newer publications:

http://www.cs.rug.nl/~biehl/Preprints/2013-Cetraro-distances.pdf

http://www.cs.rug.nl/~biehl/Preprints/linreg-ssci-2013.pdf

Border sensitive LVQ for better accuracy is considered in

http://link.springer.com/chapter/10.1007%2F978-3-642-38679-4_35

A variant of LVQ optimizing statistical measure instead of simple accuracy can be found in http://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_02_2013.pdf

If there are further questions, feel free to contact me.

Best wishes,

Thomas

A review of learning vector quantization classifiers

David Nova • Pablo A. Estevez

Neural Comput & Applic

DOI 10.1007/s00521-013-1535-3

Available online 2013