A Scalable Approach to Clustering Embedding Projections
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913843574210560 |
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| author | Ren, Donghao Hohman, Fred Moritz, Dominik |
| author_facet | Ren, Donghao Hohman, Fred Moritz, Dominik |
| contents | Interactive visualization of embedding projections is a useful technique for understanding data and evaluating machine learning models. Labeling data within these visualizations is critical for interpretation, as labels provide an overview of the projection and guide user navigation. However, most methods for producing labels require clustering the points, which can be computationally expensive as the number of points grows. In this paper, we describe an efficient clustering approach using kernel density estimation in the projected 2D space instead of points. This algorithm can produce high-quality cluster regions from a 2D density map in a few hundred milliseconds, orders of magnitude faster than current approaches. We contribute the design of the algorithm, benchmarks, and applications that demonstrate the utility of the algorithm, including labeling and summarization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Scalable Approach to Clustering Embedding Projections Ren, Donghao Hohman, Fred Moritz, Dominik Human-Computer Interaction Machine Learning Interactive visualization of embedding projections is a useful technique for understanding data and evaluating machine learning models. Labeling data within these visualizations is critical for interpretation, as labels provide an overview of the projection and guide user navigation. However, most methods for producing labels require clustering the points, which can be computationally expensive as the number of points grows. In this paper, we describe an efficient clustering approach using kernel density estimation in the projected 2D space instead of points. This algorithm can produce high-quality cluster regions from a 2D density map in a few hundred milliseconds, orders of magnitude faster than current approaches. We contribute the design of the algorithm, benchmarks, and applications that demonstrate the utility of the algorithm, including labeling and summarization. |
| title | A Scalable Approach to Clustering Embedding Projections |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2504.07285 |