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Related Experiment Video

Updated: Oct 10, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

Unsupervised random forest for affinity estimation.

Yunai Yi¹, Diya Sun¹, Peixin Li¹

¹Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, Beijing, 100871 China.

Computational Visual Media

|December 13, 2021

Summary

This summary is machine-generated.

This study introduces a novel random forest metric for unsupervised affinity estimation in complex datasets. The method enhances accuracy and consistency in data point correspondences, outperforming existing techniques.

Keywords:

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

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Last Updated: Oct 10, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Computer Science
Machine Learning
Data Mining

Background:

Affinity estimation in high-dimensional data is challenging.
Existing methods struggle with rank-deficiency and spatial relationships.

Purpose of the Study:

To develop an unsupervised, random-forest-based metric for affinity estimation.
To handle rank-deficiency and improve spatial relationship accounting.

Main Methods:

Utilized an unsupervised clustering random-forest-based metric.
Extended binary metrics to continuous ones using traversal paths and shared parent nodes.
Introduced a pseudo-leaf-splitting (PLS) algorithm for spatial regularization.

Main Results:

The proposed metric efficiently estimates affinity using a limited number of decision trees.
PLS algorithm overcomes inconsistent leaf assignments and regularizes affinity measures.
Achieved consistent and point-wise correspondences, demonstrated in automatic phrase recognition.

Conclusions:

The random-forest-based metric with PLS effectively estimates affinity in large, high-dimensional data.
The method establishes consistent point-wise correspondences.
Outperforms state-of-the-art methods in experimental comparisons.