NOFE - Neural Operator Function Embedding

TL;DR

NOFE introduces a continuous, domain-aware dimensionality reduction framework that outperforms traditional methods like PCA, t-SNE, and UMAP in local structure preservation and robustness across datasets.

Contribution

The paper presents NOFE, a novel neural operator-based method for continuous dimensionality reduction that generalizes sheaf neural networks and improves local and global structure preservation.

Findings

NOFE significantly outperforms PCA, t-SNE, and UMAP in local structure preservation.

NOFE reduces patch stitching error by up to 20 times compared to UMAP.

NOFE maintains competitive global structure preservation while resolving fine-grained details.

Abstract

Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processes. To bridge this gap, we introduce Neural Operator Function Embedding (NOFE), a domain-aware framework for continuous dimensionality reduction. NOFE learns function-to-function mappings via a Graph Kernel Operator, enabling mesh-free evaluation at arbitrary query locations independent of input discretization. We establish NOFE as approximation of sheaf-to-sheaf mappings, generalizing Sheaf Neural Networks to continuous domains. We evaluate NOFE across different datasets, comparing it against PCA, t-SNE, and UMAP. Our results demonstrate that NOFE significantly outperforms baselines in local structure preservation, achieving a local Stress of 0.111 compared to 0.398 for PCA, 0.773 for t-SNE, and 0.791 for UMAP for the ERA5 climate reanalysis dataset. NOFE also exhibits robust sampling independence, reducing the Patch Stitching Error by up to $20.0\times$ relative to UMAP (59.0 vs. 267.6 under regional normalization) and ensuring consistency across disjoint domain patches. While maintaining competitive global structure preservation (Stress-1: 0.379 vs. PCA's 0.268), NOFE resolves fine-grained structures and produces smooth, consistent embeddings that generalize across varying sample densities, addressing key limitations of discrete reduction methods.