A local approach to parameter space reduction for regression and classification tasks
This work is incremental, offering a novel clustering approach for parameter space reduction that benefits the surrogate modeling community by improving accuracy and efficiency in handling high-dimensional systems.
The authors tackled the problem of high-dimensional parameter space reduction for speeding up numerical tasks like surrogate modeling by proposing a new method called local active subspaces (LAS), which combines active subspaces with supervised clustering to achieve more efficient dimension reduction and demonstrated its effectiveness through numerical experiments.
Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and surrogate models' design, especially when in presence of high-dimensional parametrized systems. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter space. The clustering is performed without losing the input-output relations by introducing a distance metric induced by the global active subspace. We present two possible clustering algorithms: K-medoids and a hierarchical top-down approach, which is able to impose a variety of subdivision criteria specifically tailored for parameter space reduction tasks. This method is particularly useful for the community working on surrogate modelling. Frequently, the parameter space presents subdomains where the objective function of interest varies less on average along different directions. So, it could be approximated more accurately if restricted to those subdomains and studied separately. We tested the new method over several numerical experiments of increasing complexity, we show how to deal with vectorial outputs, and how to classify the different regions with respect to the local active subspace dimension. Employing this classification technique as a preprocessing step in the parameter space, or output space in case of vectorial outputs, brings remarkable results for the purpose of surrogate modelling.