Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
A corpus-based approach to comparative evaluation of statistical term association measures
Journal of the American Society for Information Science and Technology
Modern Information Retrieval
Self-Organizing Maps
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On Using Partial Supervision for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
A New Sammon Algorithm for Sparse Data Visualization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Learning from labeled and unlabeled data using a minimal number of queries
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Learning a combination of heterogeneous dissimilarities from incomplete knowledge
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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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.