Nonparametric Unsupervised Classification

TL;DR

This paper investigates the misclassification errors of popular classifiers like nearest neighbor and plug-in classifiers within unsupervised classification, highlighting the need for comprehensive performance evaluation beyond existing methods.

Contribution

It provides a novel analysis of misclassification errors for NN and plug-in classifiers in unsupervised settings, addressing a gap in existing evaluation methods.

Findings

Analyzes misclassification errors of NN and plug-in classifiers in unsupervised learning.

Highlights limitations of current unsupervised classification evaluation.

Suggests improved assessment strategies for unsupervised classifiers.

Abstract

Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an unsupervised manner. However, existing methods do not consider the misclassification error of the unsupervised classifiers except unsupervised SVM, so the performance of the unsupervised classifiers is not fully evaluated. In this work, we study the misclassification error of two popular classifiers, i.e. the nearest neighbor classifier (NN) and the plug-in classifier, in the setting of unsupervised classification.