A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces
<|reference_start|>A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces: We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages \slices from high-dimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our \slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our \slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.<|reference_end|>
@article{shienman2024a,
    title={A Slices Perspective for Incremental Nonparametric Inference in High
  Dimensional State Spaces},
    author={Moshe Shienman, Ohad Levy-Or, Michael Kaess and Vadim Indelman},
    journal={arXiv preprint arXiv:2405.16453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16453},
    primaryClass={cs.AI cs.LG}
}