Interpretable Approximation of High-Dimensional Data

Abstract

In this paper we apply the previously introduced approximation method based on the ANOVA (analysis of variance) decomposition and Grouped Transformations to synthetic and real data. The advantage of this method is the interpretability of the approximation, i.e., the ability to rank the importance of the attribute interactions or the variable couplings. Moreover, we are able to generate an attribute ranking to identify unimportant variables and reduce the dimensionality of the problem. We compare the method to other approaches on publicly available benchmark datasets.

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Cited by in corpus (4)

• HARFE: Hard-Ridge Random Feature Expansion
• Interpretable transformed ANOVA approximation on the example of the prevention of forest fires
• Sparse Mixture Models inspired by ANOVA Decompositions
• MARS via LASSO