umap-f49434a9·2 events·first seen Aliases: UMAP
EntroPath is a new manifold learning method that uses maximum entropy random walks (MERW) to build free-energy dissimilarities that converge to squared geodesic distances, addressing concentration bias in locally-normalized random walks and sensitivity to shortcut edges in shortest-path methods. The method provides a diffusion depth parameter that interpolates between local and global geometry, admits an exact Gram factorization connecting it to kernel methods, and includes scalable landmark projection extensions. Evaluations on synthetic manifolds and single-cell genomics benchmarks show EntroPath matches or outperforms diffusion- and shortest-path-based methods, with strongest gains on non-uniformly sampled manifolds and branching trajectories.
A thesis-derived arXiv preprint proposes a framework for evaluating five trajectory selection strategies—Outlierness, Diversity, Representativeness, Uncertainty, and Random—for data augmentation in spatio-temporal ML tasks. The study tests these strategies across four datasets spanning animal behavior, maritime, and urban traffic domains using linear and non-linear models with Optuna-based hyperparameter optimization. Key findings show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in sparse datasets but can degrade performance in dense, high-quality datasets, with UMAP visualization confirming topological effects.