Mixup Barcodes: Quantifying Geometric-Topological Interactions between Point Clouds

ArXi:2402.15058v3 Announce Type: replace-cross We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between them. In particular, we As a proof of concept, we apply this tool to a problem arising from machine learning. In particular, we study the disentanglement in embeddings of different classes. The results suggest that topological mixup is a useful method for characterizing interactions for low and high-dimensional data.