Global Minima by Penalized Full-dimensional Scaling
Fuente:
arXiv
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913442580922368 |
|---|---|
| author | de Leeuw, Jan |
| author_facet | de Leeuw, Jan |
| contents | The full-dimensional (metric, Euclidean, least squares) multidimensional scaling stress loss function is combined with a quadratic external penalty function term. The trajectory of minimizers of stress for increasing values of the penalty parameter is then used to find (tentative) global minima for low-dimensional multidimensional scaling. This is illustrated with several one-dimensional and two-dimensional examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Global Minima by Penalized Full-dimensional Scaling de Leeuw, Jan Computation Machine Learning 62-04 62-08 G.3 The full-dimensional (metric, Euclidean, least squares) multidimensional scaling stress loss function is combined with a quadratic external penalty function term. The trajectory of minimizers of stress for increasing values of the penalty parameter is then used to find (tentative) global minima for low-dimensional multidimensional scaling. This is illustrated with several one-dimensional and two-dimensional examples. |
| title | Global Minima by Penalized Full-dimensional Scaling |
| topic | Computation Machine Learning 62-04 62-08 G.3 |
| url | https://arxiv.org/abs/2407.16645 |