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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...
Autores principales: | Yi, Yunai, Sun, Diya, Li, Peixin, Kim, Tae-Kyun, Xu, Tianmin, Pei, Yuru |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Tsinghua University Press
2021
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Materias: | |
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 |
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