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Sparse representation for classification (SRC) has attracted much attention in recent years. It usually performs well under the following assumptions. The first assumption is that each class has sufficient training samples. In other words, SRC is not good at dealing with the undersampled problem, i.e., each class has few training samples, even single sample. The second one is that the sample vectors...
In machine learning and pattern recognition, principal component analysis (PCA) is a very popular feature extraction and dimensionality reduction method for improving recognition performance or computational effiency. It has been widely used in numerous applications, especially in face recognition. Researches often use PCA integrating the nearest neighbor classifier (NNC) based on Euclidean distance...
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