A Deep Learning Model for Spatially Clustered Data via Differentiable Cluster Assignment
We consider nonparametric regression when the association between a response and its covariates changes across an unknown partition of a spatial domain. The proposed estimator learns the partition and the cluster-specific regression functions jointly. A neural network depending only on location determines cluster membership, while separate neural networks describe the covariate--response relationship within the clusters. An annealed softmax relaxation permits gradient-based estimation of the otherwise discrete assignments. Graph-Laplacian and occupancy penalties are used to discourage fragmented regions and degenerate solutions. We establish identifiability up to label permutation, bound ...
| Authors | Kexuan Li, Weidong Ma |
|---|---|
| Primary category | stat.ML |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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