Structured sparse model based feature selection and classification for hyperspectral imagery
Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large number of feature variables exist but the amount of training samples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A linear sparse logistic regression model is proposed to combine feature selection...[Show more]
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|Source:||Structured sparse model based feature selection and classification for hyperspectral imagery|
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