ArticleComputational visual media2022
Unsupervised random forest for affinity estimation.
Article in Computational visual media, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration.bioRxiv : the preprint server for biology · 2026Article
- Article
- Machine Learning-Driven Paradigm for Polymer Aging Lifetime Prediction: Integrating Multi-Mechanism Coupling and Cross-Scale Modeling.Polymers · 2025Review
- Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study.Scientific reports · 2025Article
- Scutellarin attenuated tubule cell apoptosis by modulating HIF-1α for the treatment of DKD: the insight integrating network analysis, machine learning and single-cell transcriptome.Frontiers in pharmacology · 2025Article
- Multimodal analysis of TAAD pathogenesis: SHAP-enhanced interpretable models and single-cell sequencing analysis reveal immune microenvironment alterations.Frontiers in immunology · 2025Article
- Assessing the predictive capability of machine learning models in determining clinical outcomes for patients with cervical spondylotic myelopathy treated with laminectomy and posterior spinal fusion.Patient safety in surgery · 2024Article
- Exploring the factors influencing the use of health services by people with diabetes in Northwest China: an example from Gansu Province.Journal of health, population, and nutrition · 2023Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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. The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.
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