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arXiv Computer Vision By Piotr Keller, Fayyaz Minhas

SAIL: A Learnable Generalisation of Spatial Autocorrelation over Graph-Structured Data

Read the original on arXiv Computer Vision →

SAIL (Spatial Autocorrelation Index Learner) is a differentiable framework that extends classical spatial autocorrelation statistics, such as Moran's I and Geary's C, to high‑dimensional graph‑structured data. It learns feature projections and inter‑node neighbourhood affinities, producing interpretable local and global spatial coherence descriptors for both supervised prediction and unsupervised discovery of spatial autocorrelation modes and hotspots. Experiments on synthetic data and large‑scale biomedical graphs demonstrate that SAIL recovers traditional statistics under specific settings and can approximate Bayes‑optimal predictors for tasks driven by latent spatial autocorrelation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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