Diffusion Maps : Using the Semigroup Property for Parameter Tuning
Machine Learning
2022-03-08 v1 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
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.
Cite
@article{arxiv.2203.02867,
title = {Diffusion Maps : Using the Semigroup Property for Parameter Tuning},
author = {Shan Shan and Ingrid Daubechies},
journal= {arXiv preprint arXiv:2203.02867},
year = {2022}
}
Comments
14 pages, 12 figures