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• W2150472112 abstract "Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and high- dimensionality of the input signal space. In this paper, we propose a novel framework that overcomes these limitations by integrating learning algorithms directly with the process of signal acquisition and sampling. At the core of the proposed approach is a novel regularized max-min optimization approach that yields sigma-delta limit-cycles. An on-line adaptation modulates the limit-cycles to enhance resolution in the signal sub- spaces containing non-redundant information. Numerical experiments simulating near-singular and non-homogeneous recording conditions demonstrate consistent improvements of the proposed algorithm over a benchmark when applied for independent component analysis (ICA)." @default.
• W2150472112 created "2016-06-24" @default.
• W2150472112 creator A5029022979 @default.
• W2150472112 creator A5034498144 @default.
• W2150472112 date "2008-05-01" @default.
• W2150472112 modified "2023-09-25" @default.
• W2150472112 title "Sigma-delta learning for super-resolution independent component analysis" @default.
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• W2150472112 doi "https://doi.org/10.1109/iscas.2008.4542088" @default.
• W2150472112 hasPublicationYear "2008" @default.
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