SPreV
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866909579936268288 |
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| author | Amruth, Srivathsan |
| author_facet | Amruth, Srivathsan |
| contents | SPREV, short for hyperSphere Reduced to two-dimensional Regular Polygon for Visualisation, is a novel dimensionality reduction technique developed to address the challenges of reducing dimensions and visualizing labeled datasets that exhibit a unique combination of three characteristics: small class size, high dimensionality, and low sample size. SPREV is designed not only to uncover but also to visually represent hidden patterns within such datasets. Its distinctive integration of geometric principles, adapted for discrete computational environments, makes it an indispensable tool in the modern data science toolkit, enabling users to identify trends, extract insights, and navigate complex data efficiently and effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPreV Amruth, Srivathsan Graphics Human-Computer Interaction Machine Learning G.3 SPREV, short for hyperSphere Reduced to two-dimensional Regular Polygon for Visualisation, is a novel dimensionality reduction technique developed to address the challenges of reducing dimensions and visualizing labeled datasets that exhibit a unique combination of three characteristics: small class size, high dimensionality, and low sample size. SPREV is designed not only to uncover but also to visually represent hidden patterns within such datasets. Its distinctive integration of geometric principles, adapted for discrete computational environments, makes it an indispensable tool in the modern data science toolkit, enabling users to identify trends, extract insights, and navigate complex data efficiently and effectively. |
| title | SPreV |
| topic | Graphics Human-Computer Interaction Machine Learning G.3 |
| url | https://arxiv.org/abs/2504.10620 |