Reducing dimensionality of hyperspectral data with diffusion maps and clustering with k-means and Fuzzy ART Online publication date: Tue, 31-Mar-2015
by Louis Du Plessis; Rui Xu; Steven Damelin; Michael Sears; Donald C. Wunsch
International Journal of Systems, Control and Communications (IJSCC), Vol. 3, No. 3, 2011
Abstract: It is very difficult to analyse large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original dataset in order to obtain an efficient representation of data geometric descriptions. Clustering is done using k-means and a neural network clustering theory, Fuzzy ART (FA). The process is done on a subset of core data from AngloGold Ashanti, and compared to results obtained by AngloGold Ashanti's proprietary method. Experimental results show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples.
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