Discovering causal relationships in mixed-type non-Euclidean data with applications to fault diagnosis

Xinghan Li; Xiaokang Wang

doi:10.1117/12.3029091

6 May 2024 Discovering causal relationships in mixed-type non-Euclidean data with applications to fault diagnosis

Xinghan Li, Xiaokang Wang

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131072Y (2024) https://doi.org/10.1117/12.3029091
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

There has been a growing interest in developing machine learning algorithms that can handle non-Euclidean data. We introduce a causal generating process between parent nodes and child nodes based on multivariate tensor regression. Additionally, we propose a two-stage causal discovery approach involving regularized generalized canonical correlation analysis and greedy hill-climbing search. By utilizing numerical representation in the shared Euclidean subspace, we are able to more accurately discover causal relationships between heterogeneous non-Euclidean variables. The effectiveness of the algorithm is demonstrated using a dataset of mixed functional and compositional data, as well as empirical research conducted on real-world industrial sensor data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinghan Li and Xiaokang Wang "Discovering causal relationships in mixed-type non-Euclidean data with applications to fault diagnosis", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131072Y (6 May 2024); https://doi.org/10.1117/12.3029091

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