Unsupervised random forest for affinity estimation

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is exte...

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Detalles Bibliográficos
Autores principales: Yi, Yunai, Sun, Diya, Li, Peixin, Kim, Tae-Kyun, Xu, Tianmin, Pei, Yuru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Tsinghua University Press 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645415/
https://www.ncbi.nlm.nih.gov/pubmed/34900375
http://dx.doi.org/10.1007/s41095-021-0241-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645415/
https://www.ncbi.nlm.nih.gov/pubmed/34900375
http://dx.doi.org/10.1007/s41095-021-0241-9

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