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2008
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Sigma-delta learning for super-resolution independent component analysis

11 years 4 months ago
Sigma-delta learning for super-resolution independent component analysis
— Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and highdimensionality 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 subspaces 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).
Amin Fazel, Shantanu Chakrabartty
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where ISCAS
Authors Amin Fazel, Shantanu Chakrabartty
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