Higher Partials of fStress
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911968474955776 |
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| author | de Leeuw, Jan |
| author_facet | de Leeuw, Jan |
| contents | We define *fDistances*, which generalize Euclidean distances, squared distances, and log distances. The least squares loss function to fit fDistances to dissimilarity data is *fStress*. We give formulas and R/C code to compute partial derivatives of orders one to four of fStress, relying heavily on the use of Faà di Bruno's chain rule formula for higher derivatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Higher Partials of fStress de Leeuw, Jan Computation Methodology 62-04 62-08 91C15 G.3 We define *fDistances*, which generalize Euclidean distances, squared distances, and log distances. The least squares loss function to fit fDistances to dissimilarity data is *fStress*. We give formulas and R/C code to compute partial derivatives of orders one to four of fStress, relying heavily on the use of Faà di Bruno's chain rule formula for higher derivatives. |
| title | Higher Partials of fStress |
| topic | Computation Methodology 62-04 62-08 91C15 G.3 |
| url | https://arxiv.org/abs/2407.18314 |