# GPU-accelerated Faster Mean Shift with euclidean distance metrics

arXiv: https://arxiv.org/abs/2112.13891 · HTML: https://mlsanity.com/paper/2112.13891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [SimTriplet: Simple Triplet Representation Learning with a Single GPU](https://mlsanity.com/paper/2103.05585/) (2021)
- [Compound Figure Separation of Biomedical Images with Side Loss](https://mlsanity.com/paper/2107.08650/) (2021)
- [ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations](https://mlsanity.com/paper/2101.00567/) (2021)
- [Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space](https://mlsanity.com/paper/2011.13384/) (2020)

## Built on by (most cited first, 2 of 2)

- [Segmenting Known Objects and Unseen Unknowns without Prior Knowledge](https://mlsanity.com/paper/2209.05407/) (2022)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)

## Abstract (arXiv)

Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the mean-shift algorithm is restricted by its huge computational resource cost. In previous research[10], we proposed a novel GPU-accelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based mean-shift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available: https://github.com/masqm/Faster-Mean-Shift-Euc)