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Constrained Independent Component Analysis

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

The paper presents a novel technique of constrained independent component analysis (CICA) to introduce constraints into the clas(cid: 173) sical ICA and solve the constrained optimization problem by using Lagrange multiplier methods. This paper shows that CICA can be used to order the resulted independent components in a specific manner and normalize the demixing matrix in the signal separation procedure. It can systematically eliminate the ICA's indeterminacy on permutation and dilation. The experiments demonstrate the use of CICA in ordering of independent components while providing normalized demixing processes. Keywords: Independent component analysis, constrained indepen(cid: 173) dent component analysis, constrained optimization, Lagrange mul(cid: 173) tiplier methods

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
711677642694775943
v2026.09.13