Nonparametric Unsupervised Classification
This work addresses a gap in performance evaluation for unsupervised classifiers, which is incremental as it builds on prior methods like unsupervised SVM.
The paper tackles the problem of evaluating misclassification error in unsupervised classification, specifically for nearest neighbor and plug-in classifiers, where existing methods often overlook this aspect.
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.