A local semi-supervised Sammon algorithm for textual data visualization

Sammon’s mapping is a powerful non-linear technique that allow us to visualize high dimensional object relationships. It has been applied to a broad range of practical problems and particularly to the visualization of the semantic relations among terms in textual databases. The word maps generated by the Sammon mapping suffer from a low discriminant power due to the well known “curse of dimensionality” and to the unsupervised nature of the algorithm. Fortunately the textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we first introduce a modification of the Sammon mapping (SSammon) that enhances the local topology reducing the sensibility to the ’curse of dimensionality’. Next a semi-supervised version is proposed that takes advantage of the a priori categorization of a subset of documents to improve the discriminant power of the word maps generated. The new algorithm has been applied to the challenging problem of word map generation. The experimental results suggest that the new model improves significantly well known unsupervised alternatives.