Unsupervised random forest for affinity estimation

被引：0

作者：

Yunai Yi

Diya Sun

Peixin Li

Tae-Kyun Kim

Tianmin Xu

Yuru Pei

机构：

[1] Peking University,Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence

[2] Imperial College London,Department of Electrical and Electronic Engineering

[3] Peking University,School of Stomatology, Stomatology Hospital

来源：

Computational Visual Media | 2022年 / 8卷

关键词：

affinity estimation; forest-based metric; unsupervised clustering forest; pseudo-leaf-splitting (PLS);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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 extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.

引用

页码：257 / 272

页数：15

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