The Questionnaire approach presented here represents a meaningful advancement in data analysis and dimensionality reduction. By utilizing an informed metric, derived from partition trees, this method effectively captures the intrinsic relationships and dependencies among high-dimensional data points. In the current iterative metric construction approach, the informed updating of the appropriate metrics during the iterative process is essential: it is designed to reinforce consistency and smoothness. Exploring alternative metrics, that better capture the underlying heterogeneous structure of the data, and/or alternative measures of smoothness, could enhance the effectiveness of the method. Additionally, the computational complexity associated with constructing the diffusion map and iterating partition trees poses a significant challenge, especially for large datasets.