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Introducing higher order correlations to marginals' subset of multivariate data by means of Archimedean copulas
Krzysztof Domino, Adam Glos
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In this paper, we present the algorithm that alters the subset of marginals of multivariate standard distributed data into such modelled by an Archimedean copula. Proposed algorithm leaves a correlation matrix almost unchanged, but introduces a higher order correlation into a subset of marginals. Our data transformation algorithm can be used to analyse whether particular machine learning algorithm, especially a dimensionality reduction one, utilises higher order correlations or not. We present an exemplary application on two features selection algorithms, mention that features selection is one of the approaches to dimensionality reduction. To measure higher order correlation, we use multivariate higher order cumulants, hence to utilises higher order correlations be to use the Joint Skewness Band Selection (JSBS) algorithm that uses third-order multivariate cumulant. We show the robust performance of the JSBS in contrary to the poor performance of the Maximum Ellipsoid Volume (MEV) algorithm that does not utilise such higher order correlations. With this result, we confirm the potential application of our data generation algorithm to analyse a performance of various dimensionality reduction algorithms.
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