Independent Component Analysis Based on Non-polynomial Approximation of Negentropy: Application to MRS Source Separation

Saleh, Majd; Karfoul, Ahmad; Kachenoura, Amar; Albera, Laurent; Senhadjï, Lotfi

doi:10.1109/acssc.2018.8645115

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2024

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“…marc.castella@telecom-sudparis.eu more interesting but also much more challenging situation [6]. Contrary to many recovery methods which require precise knowledge of the transform, we deal with a blind context, assuming a linear but unknown transform, similarly to the well-known Independent Component Analysis (ICA) or related source separation techniques (see [7], [8], [9], [10], [11] or [12], [13], [14] for recent works with a similar linear model).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Linear Component Analysis for a Class of Probability Mixture Models

Castella

2024

IEEE Signal Process. Lett.

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We deal with a model where a set of observations is obtained by a linear superposition of unknown components called sources. The problem consists in recovering the sources without knowing the linear transform. We extend the well-known Independent Component Analysis (ICA) methodology. Instead of assuming independent source components, we assume that the source vector is a probability mixture of two distributions. Only one distribution satisfies the ICA assumptions, while the other one is concentrated on a specific but unknown support. Sample points from the latter are clustered based on a data-driven distance in a fully unsupervised approach. A theoretical grounding is provided through a link with the Christoffel function. Simulation results validate our approach and illustrate that it is an extension of a formerly proposed method.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Linear Component Analysis for a Class of Probability Mixture Models

Castella

2024

IEEE Signal Process. Lett.

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Independent Component Analysis Based on Non-polynomial Approximation of Negentropy: Application to MRS Source Separation

Cited by 1 publication

References 10 publications

Unsupervised Linear Component Analysis for a Class of Probability Mixture Models

Unsupervised Linear Component Analysis for a Class of Probability Mixture Models

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