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Related Experiment Videos

Initialization independent clustering with actively self-training method.

Feiping Nie¹, Dong Xu, Xuelong Li

¹Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019-0015, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 17, 2011

Summary

This summary is machine-generated.

This study introduces an active self-training clustering method that improves reliability by actively selecting training data. It enhances semisupervised learning for more accurate clustering, even with limited labels.

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Area of Science:

Machine Learning
Data Mining
Computer Science

Background:

Traditional clustering methods lack data label guidance, leading to unreliable results.
Semisupervised learning offers improved class label prediction with partial data labels.
Existing graph-based semisupervised methods struggle with Bayes error estimation.

Purpose of the Study:

To propose an actively self-training clustering method for enhanced reliability.
To explore semisupervised learning for clustering using actively selected samples.
To develop a regularization framework for effective Bayes error estimation in graph-based semisupervised learning.

Main Methods:

Active sample selection to minimize estimated Bayes error.
Development of a graph regularization framework for semisupervised learning.
Integration of active learning and semisupervised learning for clustering.

Main Results:

The proposed method demonstrates effectiveness in both unsupervised and semisupervised clustering settings.
Experimental results on toy and real-world datasets validate the approach.
The algorithm is independent of initialization, unlike traditional methods.

Conclusions:

The actively self-training clustering method offers a reliable alternative to traditional approaches.
The developed regularization framework facilitates effective Bayes error estimation.
The algorithm is versatile and applicable in semisupervised scenarios with partial labels.