Aggregating time-series and image data: functors and double functors
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915232190824448 |
|---|---|
| author | Diehl, Joscha |
| author_facet | Diehl, Joscha |
| contents | Aggregation of time-series or image data over subsets of the domain is a fundamental task in data science. We show that many known aggregation operations can be interpreted as (double) functors on appropriate (double) categories. Such functorial aggregations are amenable to parallel implementation via straightforward extensions of Blelloch's parallel scan algorithm. In addition to providing a unified viewpoint on existing operations, it allows us to propose new aggregation operations for time-series and image data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05274 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aggregating time-series and image data: functors and double functors Diehl, Joscha Category Theory Machine Learning 18D05 68W10 Aggregation of time-series or image data over subsets of the domain is a fundamental task in data science. We show that many known aggregation operations can be interpreted as (double) functors on appropriate (double) categories. Such functorial aggregations are amenable to parallel implementation via straightforward extensions of Blelloch's parallel scan algorithm. In addition to providing a unified viewpoint on existing operations, it allows us to propose new aggregation operations for time-series and image data. |
| title | Aggregating time-series and image data: functors and double functors |
| topic | Category Theory Machine Learning 18D05 68W10 |
| url | https://arxiv.org/abs/2504.05274 |