Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint

TL;DR

This paper introduces a physics-aware spatial classification method capable of handling extensive missing data by leveraging spatial structural constraints, with extensions for multi-modal feature distributions, demonstrating superior accuracy and robustness in hydrological applications.

Contribution

It extends previous physics-aware models to accommodate multi-modal feature distributions, improving classification robustness with limited observations.

Findings

Outperforms baseline methods in classification accuracy.

Multi-modal extension enhances robustness in complex feature distributions.

Efficient algorithms enable scalability to large datasets.

Abstract

Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. Existing research mostly focuses on addressing incomplete or missing data, e.g., data cleaning and imputation, classification models that allow for missing feature values or model missing features as hidden variables in the EM algorithm. These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations. To address this issue, we recently proposed a new approach that incorporates physics-aware structural constraint into the model representation. Our approach assumes that a spatial contextual feature is observed for all sample locations and establishes spatial structural constraint from the underlying spatial contextual feature map. We design efficient algorithms for model parameter learning and class inference. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. We propose learning algorithms for the extended model with multi-modal distribution. Evaluations on real-world hydrological applications show that our approach significantly outperforms baseline methods in classification accuracy, and the multi-modal extension is more robust than our early single-modal version especially when feature distribution in training samples is multi-modal. Computational experiments show that the proposed solution is computationally efficient on large datasets.