Feature Selection for Unsupervised Machine Learning.

Gaussian mixture model adjusted rand index k-means stepwise

Journal

IEEE International Conference on Smart Cloud
Titre abrégé: IEEE Int Conf Smart Cloud
Pays: United States
ID NLM: 9918804185006676

Informations de publication

Date de publication:
Sep 2023
Historique:
medline: 6 5 2024
pubmed: 6 5 2024
entrez: 6 5 2024
Statut: ppublish

Résumé

Compared to supervised machine learning (ML), the development of feature selection for unsupervised ML is far behind. To address this issue, the current research proposes a stepwise feature selection approach for clustering methods with a specification to the Gaussian mixture model (GMM) and the

Identifiants

pubmed: 38706555
doi: 10.1109/smartcloud58862.2023.00036
pmc: PMC11070246
doi:

Types de publication

Journal Article

Langues

eng

Pagination

164-169

Auteurs

Huyunting Huang (H)

Purdue University West Lafayette, Indiana.

Ziyang Tang (Z)

Purdue University West Lafayette, Indiana.

Tonglin Zhang (T)

Purdue University West Lafayette, Indiana.

Baijian Yang (B)

Purdue University West Lafayette, Indiana.

Qianqian Song (Q)

Wake Forest School of Medicine.

Jing Su (J)

Indiana University School of Medicine.

Classifications MeSH