Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations

2026-08-13

AI Summary

This work addresses the limitation of traditional tensor decomposition methods in incorporating prior knowledge from computational models when analyzing high-dimensional multi-way data, such as metabolomics datasets, which hinders the discovery of interpretable patterns. The authors propose a knowledge-guided coupled tensor decomposition framework that, for the first time, jointly analyzes real observational data and simulated data generated by computational models under linear coupling constraints. This approach enhances both robustness and interpretability of extracted patterns in noisy settings and successfully identifies latent inconsistencies between model predictions and empirical observations in real metabolomics data. The results demonstrate the method’s effectiveness and novelty in seamlessly integrating domain-specific prior knowledge with data-driven analysis.

Abstract

In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.e., with more than two axes of variation such as a subjects by metabolites by time array. While tensor factorizations have successfully revealed interpretable patterns from such complex data, they have so far been mainly data-driven. On the other hand, there is more to data -- there are computational models (of these systems), which are rich sources of prior information. In this paper, we introduce a knowledge-guided approach that brings together data and computational models by jointly analyzing real data and simulated data (generated using a computational model) using coupled tensor factorizations with linear coupling. Our experiments on real metabolomics measurements demonstrate that guiding the analysis of such noisy data with simulated data improves the pattern discovery performance while also revealing potential discrepancies between data and computational models.

Problem

• tensor factorization
• knowledge-guided learning
• multiway data
• computational models
• pattern discovery

Innovation

• knowledge-guided
• coupled tensor factorization
• computational models
• multiway data
• pattern discovery