{
  "id": "2409.01547",
  "version": "v1",
  "published": "2024-09-03T02:34:42.000Z",
  "updated": "2024-09-03T02:34:42.000Z",
  "title": "The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines",
  "authors": [
    "Jungmin Shin",
    "Seung Jun Shin",
    "Andrea Artemiou"
  ],
  "categories": [
    "stat.CO",
    "stat.ML"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-09-03T02:34:42.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "sufficient dimension reduction",
      "principal machine",
      "unified algorithm",
      "sdr estimators",
      "principal support vector machine"
    ],
    "tags": [
      "research tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}