Dimensionality reduction is an integral part of data visualization. It is a process that obtains a structure preserving low-dimensional representation of the high-dimensional data. Two common criteria can be used to achieve a dimensionality reduction: distance preservation and topology preservation. Inspired by recent work in topological data analysis, we are on the quest for a dimensionality reduction technique that achieves the criterion of homology preservation, a specific version of topology preservation. Specifically, we are interested in using topology-inspired manifold landmarking and manifold tearing to aid such a process and evaluate their effectiveness.

Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing

L Yan, Y Zhao, P Rosen, C Scheidegger, B Wang

Visualization in Data Science (VDS at IEEE VIS 2018)