Ensemble Learning for Mapper Parameter Optimization

The Mapper algorithm is a technique from TDA used to create low-dimensional graph-based representations of high-dimensional data, proven effective in numerous exploratory data analysis tasks. The Mapper algorithm's output depends on several user-chosen parameters, and selecting their values is a non-trivial choice, significantly narrowing its potential application in real-world scenarios. Research attempting to assist in selection of the parameters has been very limited to date. This paper is the first one to address the selection of Mapper's three parameters simultaneously. The proposed idea incorporates the concept of Ensemble Learning into the Mapper algorithm. Using several datasets with known labels, we show that our method outperforms two baselines in recovering the dataset structure.