Interpretable Approximation of High-Dimensional Data
This work addresses the need for interpretable dimensionality reduction in data analysis, but it is incremental as it applies a previously introduced method without major innovations.
The paper tackles the problem of approximating high-dimensional data by applying an existing ANOVA-based method to synthetic and real datasets, resulting in interpretable approximations that rank attribute importance and reduce dimensionality, with comparisons to other approaches on benchmarks.
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.