Group Invariant Spectral Embedding
The approach modifies graph Laplacians by using affinity kernels that respect symmetries of a data manifold, defined by a group G. This allows the graph Laplacian to converge to explicit differential operators on the quotient space, leading to faster convergence and better recovery of the data's true intrinsic geometry when symmetries are known.