Semi-supervised metrics for textual data visualization

Abstract

Multidimensional Scaling algorithms (MDS) are useful tools that help to discover high dimensional object relationships. They have been applied to a wide range of practical problems and particularly to the visualization of the semantic relations among documents or terms in textual databases. The MDS algorithms proposed in the literature often suffer from a low discriminant power due to its unsupervised nature and to the 'curse of dimensionality'. Fortunately, textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we propose a semi-supervised version of the Torgerson MDS algorithm that takes advantage of this document classification to improve the discriminant power of the word maps generated. The algorithm has been applied to the visualization of term relationships. The experimental results show that the model proposed outperforms well known unsupervised alternatives. © Springer-Verlag Berlin Heidelberg 2007.