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Semi-supervised nearest mean classification through a constrained log-likelihood.

Marco Loog, Are Charles Jensen

IEEE Transactions on Neural Networks and Learning Systems

|April 17, 2015

Summary

This summary is machine-generated.

This study introduces a constrained semi-supervised nearest mean classifier. The new method consistently improves log-likelihood, outperforming supervised methods and self-learning approaches in classification tasks.

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Area of Science:

Machine Learning
Statistical Classification

Background:

Semi-supervised learning offers a powerful approach when labeled data is scarce.
Nearest mean classifiers provide a simple yet effective classification strategy.
Existing semi-supervised methods may not always optimize the intended risk metrics.

Purpose of the Study:

To reformulate a semi-supervised nearest mean classifier within a constrained log-likelihood framework.
To evaluate the performance of the constrained classifier against supervised and other semi-supervised methods.
To emphasize the importance of considering optimized risk beyond just error rates in semi-supervised learning.

Main Methods:

Development of a principled log-likelihood formulation for a nearest mean classifier.
Implementation of constraints within the classifier's objective function.
Empirical comparison of the constrained semi-supervised nearest mean classifier with supervised nearest mean classification and self-learning.

Main Results:

The constrained semi-supervised nearest mean classifier consistently outperforms its supervised counterpart in terms of test set log-likelihood.
Classification error rates show mixed results when comparing supervised and semi-supervised nearest mean classification.
The study clarifies the advantages of the constrained approach over standard supervised nearest mean classification.

Conclusions:

A constrained semi-supervised nearest mean classifier offers significant advantages in optimizing log-likelihood.
Evaluating semi-supervised learners should encompass both error rates and the risk they aim to optimize.
The proposed method provides a valuable improvement over traditional supervised and self-learning techniques.