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Semantic Mapping (Statistics)
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High Quality Content by WIKIPEDIA articles! 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…mehr

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High Quality Content by WIKIPEDIA articles! 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.