Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations
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Computer Science > Machine Learning
Title:Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.13234 [cs.LG] |
| (or arXiv:2608.13234v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13234
arXiv-issued DOI via DataCite (pending registration)
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