mlsanityData to 14 Oct 2026

Diffusion Maps : Using the Semigroup Property for Parameter Tuning

arXiv 2203.02867 ↗First posted 6 Mar 2022stat.ML cs.LG cs.NAcited by 3

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Abstract (arXiv)

Diffusion maps (DM) constitute a classic dimension reduction technique, for data lying on or close to a (relatively) low-dimensional manifold embedded in a much larger dimensional space. The DM procedure consists in constructing a spectral parametrization for the manifold from simulated random walks or diffusion paths on the data set. However, DM is hard to tune in practice. In particular, the task to set a diffusion time t when constructing the diffusion kernel matrix is critical. We address this problem by using the semigroup property of the diffusion operator. We propose a semigroup criterion for picking t. Experiments show that this principled approach is effective and robust.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 0 of its 13 references that are in the map. First posted on arXiv 6 Mar 2022.

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

3 papers.