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Hyperspectral remote sensing image is a typical high-dimensional data with a large number of redundant informantion, which will impact the classification accuracy. Feature extraction is an effective method to reduce the redundancy of hyperspectral image (HSI) and improve the classification performance. However, most feature extraction methods just consider a single structure information of HSI that...
Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness...
Marginal Fisher Analysis(MFA) is a typical supervised subspace embedding method which has been used in dimensionality reduction. The projection matrixes are obtained by maximizing the intraclass compactness and simultaneously minimizing the intraclass separability. But in practical applications, no sufficient labeled training samples with prior knowledge was provided, so unlabeled image data are eager...
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