Nested Fusion: A Method for Learning High Resolution Latent Structure of Multi-Scale Measurement Data on Mars
<|reference_start|>Nested Fusion: A Method for Learning High Resolution Latent Structure of Multi-Scale Measurement Data on Mars: The Mars Perseverance Rover represents a generational change in the scale of measurements that can be taken on Mars, however this increased resolution introduces new challenges for techniques in exploratory data analysis. The multiple different instruments on the rover each measures specific properties of interest to scientists, so analyzing how underlying phenomena affect multiple different instruments together is important to understand the full picture. However each instrument has a unique resolution, making the mapping between overlapping layers of data non-trivial. In this work, we introduce Nested Fusion, a method to combine arbitrarily layered datasets of different resolutions and produce a latent distribution at the highest possible resolution, encoding complex interrelationships between different measurements and scales. Our method is efficient for large datasets, can perform inference even on unseen data, and outperforms existing methods of dimensionality reduction and latent analysis on real-world Mars rover data. We have deployed our method Nested Fusion within a Mars science team at NASA Jet Propulsion Laboratory (JPL) and through multiple rounds of participatory design enabled greatly enhanced exploratory analysis workflows for real scientists. To ensure the reproducibility of our work we have open sourced our code on GitHub at https://github.com/pixlise/NestedFusion.<|reference_end|>
@article{wright2024nested,
    title={Nested Fusion: A Method for Learning High Resolution Latent Structure of
  Multi-Scale Measurement Data on Mars},
    author={Austin P. Wright, Scott Davidoff, Duen Horng Chau},
    journal={In Proceedings of the 30th ACM SIGKDD Conference on Knowledge
  Discovery and Data Mining (KDD '24), August 25-29, 2024, Barcelona, Spain.
  ACM, New York, NY, USA},
    year={2024},
    doi={10.1145/3637528.3671596},
    archivePrefix={arXiv},
    eprint={2409.05874},
    primaryClass={cs.CE stat.AP}
}