Latent semantic indexing is an optimal special case of multidimensional scaling
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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IEEE Transactions on Knowledge and Data Engineering
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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Proceedings of the 1st ACM International Health Informatics Symposium
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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.