Semantic mapping (statistics): Difference between revisions
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The '''semantic mapping (SM)''' is a [[dimensionality reduction]] method that extracts new features by [[clustering]] the original features in semantic [[cluster]]s 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]]s 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 [[Euclidean vector|vector]]s of high dimensionality. |
The '''semantic mapping (SM)''' is a [[dimensionality reduction]] method that extracts new features by [[clustering]] the original features in semantic [[cluster]]s 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]]s 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 [[Euclidean vector|vector]]s of high dimensionality. |
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The '''SM''' is an alternative to [[principal components analysis]] and [[latent semantic indexing]] methods. |
The '''SM''' is an alternative to [[principal components analysis]] and [[latent semantic indexing]] methods. |
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Revision as of 16:24, 15 November 2010
 | 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.