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Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dimensional manifold defined by local geometric distances between samples. Most of the traditional methods have focused only on the spectral distances in calculating the local dissimilarity of samples, whereas in the case of image data, the spatial distribution and localized contextual information of image samples could provide useful information. As a framework for integrating spatialdoi:10.1109/igarss.2007.4423667 dblp:conf/igarss/KimCCTG07 fatcat:x6x432jwandk5im75m7pu7iqca