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- W2155752516 abstract "The presence of large amounts of data in hyperspectral images makes it very difficult to perform further tractable analyses. Here, we present a method of analyzing real hyperspectral data by dimensionality reduction using diffusion maps. Diffusion maps interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain an efficient representation of data geometric descriptions. A neural network clustering theory, Fuzzy ART, is further applied to the reduced data to form clusters of the potential minerals. Experimental results on a subset of hyperspectral core imager data show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples." @default.
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- W2155752516 date "2009-06-01" @default.
- W2155752516 modified "2023-09-26" @default.
- W2155752516 title "Analysis of hyperspectral data with diffusion maps and Fuzzy ART" @default.
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- W2155752516 doi "https://doi.org/10.1109/ijcnn.2009.5178910" @default.
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