Marginal median SOM for document organization and retrieval

Authors:
A. Georgakis;C. Kotropoulos;A. Xafopoulos;I. Pitas
Affiliations:
Digital Media Laboratory (DML), Dept. of App. Phys. and Elec., Umeå Univ., Umeå SE-90187, Sweden and Artif. Intell. and Info. Anal. Lab., Dept. of Info., Aristotle Univ. of Thessaloniki, ...;Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki GR-54124, Greece;Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki GR-54124, Greece;Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki GR-54124, Greece
Venue:
Neural Networks
Year:
2004

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Abstract

The self-organizing map algorithm has been used successfully in document organization. We now propose using the same algorithm for document retrieval. Moreover, we test the performance of the self-organizing map by replacing the linear Least Mean Squares adaptation rule with the marginal median. We present two implementations of the latter variant of the self-organizing map by either quantizing the real valued feature vectors to integer valued ones or not. Experiments performed using both implementations demonstrate a superior performance against the self-organizing map based method in terms of the number of training iterations needed so that the mean square error (i.e. the average distortion) drops to the e-1 = 36.788% of its initial value. Furthermore, the performance of a document organization and retrieval system employing the self-organizing map architecture and its variant is assessed using the average recall-precision curves evaluated on two corpora; the first comprises of manually selected web pages over the Internet having touristic content and the second one is the Reuters- 21578, Distribution 1.0.