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Small amount of training data confines the performance of auto noise classification system, especially when the dimensions of features are in a large scale. In this paper, 26-dimensional features are extracted from cavitation noise spectrum and line spectrum from three classes of cavitation noises. Principal component analysis (PCA) based method is applied to deal with the high-dimensional features...
Support vector machine (SVM) appears to be a robust alternative for pattern recognition with hyperspectral data. However, this kernel-based method does not take into consideration the bio-physical meaning of the spectral signatures. Observation of real-life spectral signatures from the AVIRIS hyperspectral dataset shows that the useful information for classification is not equally distributed across...
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