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We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping...
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...
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