Semantic mapping (statistics)
 | This article provides insufficient context for those unfamiliar with the subject. (November 2010) |
The semantic mapping (SM) is a dimensionality reduction method that extracts new features 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 elements 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 principal components analysis and latent semantic indexing methods.
See also
References
- 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.