Ajit Kumar

Topological Clustering via Sliced Wasserstien Kernels

A kernel-means algorithm built on a convex combination of sliced Wasserstein (SW) kernels, one for each homology under consideration, which outperforms the baselines on two benchmark datasets and remains competitive on a third synthetic dataset, while being computationally efficient.