Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension

Diffusion maps are a nonlinear manifold learning technique based on harmonic analysis of a diffusion process over the data. Out-of-sample extensions with computational complexity \(\mathcal{O}(N)\), where \(N\) is the number of points comprising the manifold, frustrate applications to online learning applications requiring rapid embedding of high-dimensional data streams. We propose landmark diffusion maps (L-dMaps) to reduce the complexity to \(\mathcal{O}(M)\), where \(M \ll N\) is the number of landmark points selected using pruned spanning trees or k-medoids. Offering \((N/M)\) speedups in out-of-sample extension, L-dMaps enables the application of diffusion maps to high-volume and/or high-velocity streaming data. We illustrate our approach on three datasets: the Swiss roll, molecular simulations of a C\(_{24}\)H\(_{50}\) polymer chain, and biomolecular simulations of alanine dipeptide. We demonstrate up to 50-fold speedups in out-of-sample extension for the molecular systems with less than 4% errors in manifold reconstruction fidelity relative to calculations over the full dataset.