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An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model

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Abstract

This paper introduces an approximate nuclear norm based matrix regression projection (ANMRP) model, an adaptive graph embedding method, for feature extraction of hyperspectral images. The ANMRP utilizes an approximate NMR model to construct an adaptive neighborhood map between samples. The globally optimal weight matrix is obtained by optimizing the approximate NMR model using fast alternating direction method of multipliers (ADMM). The optimal projection matrix is then determined by maximizing the ratio of the local scatter matrix to the total scatter matrix, allowing for the extraction of discriminative features. Experimental results demonstrate the effectiveness of ANMRP compared to related methods.

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Authors and Affiliations

  1. College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo, 315200, China

    Hong Qiu, Renfang Wang & Feng Wang

  2. College of Information, Shanghai Ocean University, Shanghai, 200120, China

    Heng **

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  1. Hong Qiu
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  2. Renfang Wang
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  3. Heng **
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  4. Feng Wang
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Correspondence to Renfang Wang.

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Conflicts of interest

The authors declare no conflict of interest.

This work has been supported by the National Natural Science Foundation of China (No.61906170), the Project of the Science and Technology Plan for Zhejiang Province (No.LGF21F020023), and the Plan Project of Ningbo Municipal Science and Technology (Nos.2022Z233, 2021Z050, 2022S002 and 2023J403).

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Qiu, H., Wang, R., **, H. et al. An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model. Optoelectron. Lett. 19, 443–448 (2023). https://doi.org/10.1007/s11801-023-3054-5

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  • DOI: https://doi.org/10.1007/s11801-023-3054-5

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