Semantic mapping (statistics)
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The semantic mapping (SM) is a dimensionality reduction method that can be used in a set of multidimensional vectors of features to extracts few new features that preserves main data characteristics. SM perform dimensionality reduction by clustering the original features in semantic clusters and combining features mapped in the same cluster to generate an extracted feature. Given a data set, this method construct a projection matrix that can be used to mapping of data element from one high dimensional space into reduced dimensional space. The SM can be applied in construction of text mining and information retrieval systems, as well as systems managing vectors of high dimensionality. The SM is an alternative to random mapping, principal components analysis and latent semantic indexing methods.
- CORRÊA, R. F.; LUDERMIR, T. B. Improving Self Organization of Document Collections by Semantic Mapping. Neurocomputing(Amsterdam), v. 70, p. 62-69, 2006. doi:10.1016/j.neucom.2006.07.007
- CORRÊA, R. F. and LUDERMIR, T. B. (2007) "Dimensionality Reduction of very large document collections by Semantic Mapping". Proceedings of 6th Int. Workshop on Self-Organizing Maps (WSOM). ISBN 978-3-00-022473-7.