A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization

author: Manuel Martin-Merino Acera, Pontifical University of Salamanca
published: Oct. 8, 2007,   recorded: September 2007,   views: 5630


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Multidimensional Scaling Algorithms (MDS) allow us to visualize high dimensional object relationships in an intuitive way. An interesting application of the MDS algorithms is the visualization of the semantic relations among documents or terms in textual databases. However, the MDS algorithms proposed in the literature exhibit a low discriminant power. The unsupervised nature of the algorithms and the ’curse of dimensionality’ favor the overlapping among different topics in the map. This problem can be overcome considering that many textual collections provide frequently a categorization for a small subset of documents. In this paper we define new semi-supervised measures that reflect better the semantic classes of the textual collection considering the a priori categorization of a subset of documents. Next the dissimilarities are incorporated into the Torgerson MDS algorithm to improve the separation among topics in the map. The experimental results show that the model proposed outperforms well known unsupervised alternatives.

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Reviews and comments:

Comment1 ArtMan, February 19, 2009 at 2:57 p.m.:

Dios! El puto jefe! Que vean como nos las gastamos en la Ponti, ese es mi Manolo!
Siemre he dicho que de mayor queria ser como él y ahora mas que nunca.

Gracias Manolo!

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