Combining distance measures in Learning Vector Quantization

Bearda, M.G. (2011) Combining distance measures in Learning Vector Quantization. Master's Thesis / Essay, Computing Science.

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Abstract

This thesis looks into Learning Vector Quantization where multiple distance measures are combined. Herein, besides prototypes, combinations between these distance measures are learned. This gave an intuitive solution to learn combinations between histograms of the different color components of an image. I tested the algorithm on images from dermatology provided by the University Medical Center Groningen, Netherlands. Secondly I tested the algorithm on images of leafs possibly infected with the Cassava Mosaic Disease provided by the Namulonge National Crops Resources Research Institute, Uganda. I found that it is not preferable to use the space spanned by the combination matrix for sample-sample distances. The space is namely spanned for prototype-sample distances. And unlike other LVQ algorithms, which use one single distance measure, arbitrarily distance measures can give high performance for prototype distances but low performances on distances between two samples of the same class. Secondly I concluded that, when the prototypes are used, the algorithm is a useful new Learning Vector Quantization variant that can give outstanding performances.

Item Type:	Thesis (Master's Thesis / Essay)
Degree programme:	Computing Science
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	15 Feb 2018 07:46
Last Modified:	15 Feb 2018 07:46
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/9778

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