An adaptive graph embedding method for feature ex-traction of hyperspectral images based on approximate NMR model
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Abstract:
This paper introduces ANMRP, an adaptive graph embedding method for feature extraction of hyperspectral images. ANMRP utilizes an approximate NMR model to construct an adaptive neighborhood map between samples. The glob-ally optimal weight matrix is obtained by optimizing the approximate NMR model using fast ADMM. The optimal projection matrix is then determined by maximizing the ratio of the local scatter matrix to the total scatter matrix, al-lowing for the extraction of discriminative features. Experimental results demonstrate the effectiveness of ANMRP compared to related methods.
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Project Supported:
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
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