Marginal median SOM for document organization and retrieval
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 recallprecision 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.
marginal median self-organizing maps order statistics