Formation-Controlled Dimensionality Reduction
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917893279580160 |
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| author | Jeong, Taeuk Jung, Yoon Mo Lee, Euntack |
| author_facet | Jeong, Taeuk Jung, Yoon Mo Lee, Euntack |
| contents | Dimensionality reduction represents the process of generating a low dimensional representation of high dimensional data. Motivated by the formation control of mobile agents, we propose a nonlinear dynamical system for dimensionality reduction. The system consists of two parts; the control of neighbor points, addressing local structures, and the control of remote points, accounting for global structures.We also include a brief mathematical analysis of the model and its numerical procedure. Numerical experiments are performed on both synthetic and real datasets and comparisons with existing models demonstrate the soundness and effectiveness of the proposed model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Formation-Controlled Dimensionality Reduction Jeong, Taeuk Jung, Yoon Mo Lee, Euntack Machine Learning Dimensionality reduction represents the process of generating a low dimensional representation of high dimensional data. Motivated by the formation control of mobile agents, we propose a nonlinear dynamical system for dimensionality reduction. The system consists of two parts; the control of neighbor points, addressing local structures, and the control of remote points, accounting for global structures.We also include a brief mathematical analysis of the model and its numerical procedure. Numerical experiments are performed on both synthetic and real datasets and comparisons with existing models demonstrate the soundness and effectiveness of the proposed model. |
| title | Formation-Controlled Dimensionality Reduction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.06808 |