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This paper proposes the kernel entropy component analysis (KECA) for clustering remote sensing data. The method generates nonlinear features that reveal structure related to the Rényi entropy of the input space data set. Unlike other kernel feature extraction methods, the top eigenvalues and eigenvectors of the kernel matrix are not necessarily chosen. Data are interestingly mapped with a distinct...
This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert...
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