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• W2094670612 abstract "This paper proposes a novel framework that is capable of extracting the low-rank interference while simultaneously promoting sparsity-based representation of multiple correlated signals. The proposed model provides an efficient approach for the representation of multiple measurements where the underlying signals exhibit a structured sparsity representation over some proper dictionaries but the set of testing samples are corrupted by the interference from external sources. Under the assumption that the interference component forms a low-rank structure, the proposed algorithms minimize the nuclear norm of the interference to exclude it from the representation of mul-tivariate sparse representation. An efficient algorithm based on alternating direction method of multipliers is proposed for the general framework. Extensive experimental results are conducted on two practical applications: chemical plume detection and classification in hyperspectral sequences and robust speech recognition in noisy environments to verify the effectiveness of the proposed methods." @default.
• W2094670612 created "2016-06-24" @default.
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• W2094670612 date "2014-11-01" @default.
• W2094670612 modified "2023-09-25" @default.
• W2094670612 title "Structured sparse representation with low-rank interference" @default.
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• W2094670612 doi "https://doi.org/10.1109/acssc.2014.7094407" @default.
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