{
  "abstract": "We propose a novel approach for clustering that is later extended to classification. Our methods are based on an underlying determinantal point process (DPP) that generates sparsity and separation of location parameters in a mixture of finite mixture models specification. A key aspect is the assumption of a DPP conditional on the number of groups and a prior on this number, which results in a dynamic mixture of repulsive mixtures. The proposed model also involves a sparse tensor decomposition of the covariance function used to define the DPP, which is coupled with a separate mixture model employed to handle the intra-cluster correlation structure. Further computational enhancement in the high-dimensional case comes from exploiting a mapping from Euclidean space to the space of (valid) correlation matrices. A suitable posterior simulation scheme involving a modified telescopic sampler is developed. The resulting approach is seen to perform satisfactorily in simulated and real data examples.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37607",
  "author_total": 2,
  "authors": [
    {
      "name": "Alejandro Murua"
    },
    {
      "name": "Fernando Andrés Quintana"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0mk176t6zt47h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37607",
  "fulltext_url": null,
  "id": "0mk176t6zt47h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.37607",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Bayesian Methods and Mixture Models"
    ]
  ],
  "openalex": "W7224135216",
  "pdf_url": "https://arxiv.org/pdf/2610.37607",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0mk176t6zt47h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2610.37607",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37607"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A flexible approach to high-dimensional clustering and classification using mixtures of finite mutually repulsive mixtures",
  "title_text": "A flexible approach to high-dimensional clustering and classification using mixtures of finite mutually repulsive mixtures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/0mk176t6zt47h",
  "venue": "arXiv",
  "versions": []
}