Topological Data Analysis (TDA) is an approach to handle with big data by studying its shape. A main tool of TDA is the persistence diagram, and one can use it to compare data sets. One approach to learn on the similarity between two persistence diagrams is to use the Bottleneck and the Wasserstein distances. Another approach is to fit a parametric model for each diagram, and then to compare the model coefficients. We study the behavior of both distance measures and the RST parametric model. The theoretical behavior of the distance measures is difficult to be developed, and therefore we study their behavior numerically. We conclude that the RST model has an advantage over the Bottleneck and the Wasserstein distances in sense that it can give a definite conclusion regarding the similarity between two persistence diagrams. More of that, a great advantage of the RST is its ability to distinguish between two data sets that are geometrically different but topologically are the same, which is impossible to have by the two distance measures.
|Communications in Statistics Part B: Simulation and Computation
|Accepted/In press - 2021
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- Bottleneck distance
- Persistence diagram
- Wasserstein distance