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Independent component analysis via copula techniques

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Independent component analysis (ICA) is a modern factor analysis tool developed in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then independent components are found by optimizing the copula parameters. Based on this idea, we propose the COPICA method for searching independent components. We illustrate this method using several blind source separation examples, which are mathematically equivalent to ICA problems. Finally performances of our method and FastICA are compared to explore the advantages of this method.

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Ray-Bing Chen, Meihui Guo, Wolfgang Karl Härdle, Shih-Feng Huang

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Adapt according to the presented license agreement and reference the original author.