{
  "abstract": "Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data. Conditional Multidimensional Scaling (cMDS) is a cDR technique that works directly with dissimilarity data. Its standard squared-stress formulation, however, is sensitive to contaminated dissimilarity, since outliers can dominate the objective and distort the learned configuration. We proposed Robust Conditional Multidimensional Scaling (rcMDS) by replacing the squared-stress criterion with a Fair M-estimation objective. We developed a reweighted conditional SMACOF algorithm to optimize this objective. The proposed algorithm admits computationally tractable updates, and its stabilized objective values decrease monotonically and converge to a finite limit. Experiments on synthetic and real data show that the pro",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.06284",
  "author_total": 2,
  "authors": [
    {
      "name": "Xiao Ling"
    },
    {
      "name": "Anh Bui"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dt1egx20pjzsa.bib",
  "cited_by": 1,
  "doi": "10.48550/arxiv.2609.06284",
  "fulltext_url": null,
  "id": "dt1egx20pjzsa",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.06284",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1707|Face and Expression Recognition"
    ]
  ],
  "openalex": "W7212020093",
  "pdf_url": "https://arxiv.org/pdf/2609.06284",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dt1egx20pjzsa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "2609.06284",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.06284"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Robust conditional dimension reduction for dissimilarity data",
  "title_text": "Robust conditional dimension reduction for dissimilarity data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/dt1egx20pjzsa",
  "venue": "arXiv",
  "versions": []
}