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Communication Dans Un Congrès Année : 2016

ADMM for Maximum Correntropy Criterion

Fei Zhu
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Abderrahim Halimi
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Paul Honeine

Résumé

The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra called endmembers. The coefficients of the combination need to be estimated subject to the nonnegativity and the sum-to-one constraints. In practice, some spectral bands suffer from low signal-to-noise ratio due to acquisition noise and atmospheric effects, thus requiring robust techniques for the unmixing problem. In this work, we cast the unmixing problem as the maximization of a correntropy criterion, and provide a relevant solution using the alternating direction method of multipliers (ADMM) method. Finally, the relevance of the proposed approach is validated on synthetic and real hyperspectral images, demonstrating that the correntropy-based unmixing is robust to outlier bands.
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Dates et versions

hal-01965916 , version 1 (27-12-2018)

Identifiants

Citer

Fei Zhu, Abderrahim Halimi, Paul Honeine, Badong Chen, Nanning Zheng. ADMM for Maximum Correntropy Criterion. Proc. 28th (INNS and IEEE-CIS) International Joint Conference on Neural Networks, 2016, Vancouver, Canada. pp.1420-1427, ⟨10.1109/IJCNN.2016.7727365⟩. ⟨hal-01965916⟩
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