Interpretable Approximation of High-Dimensional Data

TL;DR

This paper presents an interpretable approximation method for high-dimensional data using ANOVA decomposition and grouped transformations, enabling variable importance ranking and dimensionality reduction.

Contribution

It introduces a novel, interpretable approximation technique that effectively ranks attribute interactions and reduces dimensionality, validated on benchmark datasets.

Findings

Effective attribute importance ranking

Successful dimensionality reduction

Competitive performance on benchmarks

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.