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- W2073984211 abstract "Two methods for linear mapping of multidimensional data in the case of unsupervised learning are proposed. The first method maximizes the mean square density gradient of the projected samples with the intention of compressing the clusters. The second method is based on the k-NN technique and obtains a map of the scatter of the neighbor clusters. An experiment with the classical Iris data shows the mapping accuracy of the latter method." @default.
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- W2073984211 title "Linear mappings of local data structures" @default.
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- W2073984211 doi "https://doi.org/10.1016/0167-8655(92)90054-4" @default.
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