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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.05682 |
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| _version_ | 1866909274615054336 |
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| author | Lichtenberg, Samuel Tasissa, Abiy |
| author_facet | Lichtenberg, Samuel Tasissa, Abiy |
| contents | Classical multidimensional scaling (CMDS) is a technique that embeds a set of objects in a Euclidean space given their pairwise Euclidean distances. The main part of CMDS involves double centering a squared distance matrix and using a truncated eigendecomposition to recover the point coordinates. In this paper, motivated by a study in Euclidean distance geometry, we explore a dual basis approach to CMDS. We give an explicit formula for the dual basis vectors and fully characterize the spectrum of an essential matrix in the dual basis framework. We make connections to a related problem in metric nearness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05682 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A dual basis approach to multidimensional scaling Lichtenberg, Samuel Tasissa, Abiy Spectral Theory Information Theory Machine Learning Classical multidimensional scaling (CMDS) is a technique that embeds a set of objects in a Euclidean space given their pairwise Euclidean distances. The main part of CMDS involves double centering a squared distance matrix and using a truncated eigendecomposition to recover the point coordinates. In this paper, motivated by a study in Euclidean distance geometry, we explore a dual basis approach to CMDS. We give an explicit formula for the dual basis vectors and fully characterize the spectrum of an essential matrix in the dual basis framework. We make connections to a related problem in metric nearness. |
| title | A dual basis approach to multidimensional scaling |
| topic | Spectral Theory Information Theory Machine Learning |
| url | https://arxiv.org/abs/2303.05682 |