The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916382733500416 |
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| author | Shin, Jungmin Shin, Seung Jun Artemiou, Andreas |
| author_facet | Shin, Jungmin Shin, Seung Jun Artemiou, Andreas |
| contents | Sufficient dimension reduction (SDR), which seeks a lower-dimensional subspace of the predictors containing regression or classification information has been popular in a machine learning community. In this work, we present a new R software package psvmSDR that implements a new class of SDR estimators, which we call the principal machine (PM) generalized from the principal support vector machine (PSVM). The package covers both linear and nonlinear SDR and provides a function applicable to realtime update scenarios. The package implements the descent algorithm for the PMs to efficiently compute the SDR estimators in various situations. This easy-to-use package will be an attractive alternative to the dr R package that implements classical SDR methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines Shin, Jungmin Shin, Seung Jun Artemiou, Andreas Computation Machine Learning Sufficient dimension reduction (SDR), which seeks a lower-dimensional subspace of the predictors containing regression or classification information has been popular in a machine learning community. In this work, we present a new R software package psvmSDR that implements a new class of SDR estimators, which we call the principal machine (PM) generalized from the principal support vector machine (PSVM). The package covers both linear and nonlinear SDR and provides a function applicable to realtime update scenarios. The package implements the descent algorithm for the PMs to efficiently compute the SDR estimators in various situations. This easy-to-use package will be an attractive alternative to the dr R package that implements classical SDR methods. |
| title | The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2409.01547 |