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Target detection has been of great interest in hyperspectral image analysis. Feature extraction from target samples and counterpart backgrounds consist the key to the problem. Traditional target detection methods depend on comparatively fixed feature for all the pixels under observation. For example, RX employs the same distance measurement for all the pixels. However, the best separation results...
In this paper, we kernelize conventional clustering algorithms from a novel point of view. Based on the fully mathematical proof, we first demonstrate that kernel KMeans (KKMeans) is equivalent to kernel principal component analysis (KPCA) prior to the conventional KMeans algorithm. By using KPCA as a preprocessing step, we also generalize Gaussian mixture model (GMM) to its kernel version, the kernel...
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