Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.16453 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910460046999552 |
|---|---|
| author | Shienman, Moshe Levy-Or, Ohad Kaess, Michael Indelman, Vadim |
| author_facet | Shienman, Moshe Levy-Or, Ohad Kaess, Michael Indelman, Vadim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces Shienman, Moshe Levy-Or, Ohad Kaess, Michael Indelman, Vadim Artificial Intelligence Machine Learning 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. |
| title | A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.16453 |