Independent Component Analysis by Robust Distance Correlation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913837001736192 |
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| author | Leyder, Sarah Raymaekers, Jakob Rousseeuw, Peter J. Van Deuren, Tom Verdonck, Tim |
| author_facet | Leyder, Sarah Raymaekers, Jakob Rousseeuw, Peter J. Van Deuren, Tom Verdonck, Tim |
| contents | Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunately, most approaches to ICA are not robust against outliers. Here we propose a robust ICA method called RICA, which estimates the components by minimizing a robust measure of dependence between multivariate random variables. The dependence measure used is the distance correlation (dCor). In order to make it more robust we first apply a new transformation called the bowl transform, which is bounded, one-to-one, continuous, and maps far outliers to points close to the origin. This preserves the crucial property that a zero dCor implies independence. RICA estimates the independent sources sequentially, by looking for the component that has the smallest dCor with the remainder. RICA is strongly consistent and has the usual parametric rate of convergence. Its robustness is investigated by a simulation study, in which it generally outperforms its competitors. The method is illustrated on three applications, including the well-known cocktail party problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Independent Component Analysis by Robust Distance Correlation Leyder, Sarah Raymaekers, Jakob Rousseeuw, Peter J. Van Deuren, Tom Verdonck, Tim Computation Machine Learning Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunately, most approaches to ICA are not robust against outliers. Here we propose a robust ICA method called RICA, which estimates the components by minimizing a robust measure of dependence between multivariate random variables. The dependence measure used is the distance correlation (dCor). In order to make it more robust we first apply a new transformation called the bowl transform, which is bounded, one-to-one, continuous, and maps far outliers to points close to the origin. This preserves the crucial property that a zero dCor implies independence. RICA estimates the independent sources sequentially, by looking for the component that has the smallest dCor with the remainder. RICA is strongly consistent and has the usual parametric rate of convergence. Its robustness is investigated by a simulation study, in which it generally outperforms its competitors. The method is illustrated on three applications, including the well-known cocktail party problem. |
| title | Independent Component Analysis by Robust Distance Correlation |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2505.09425 |