By Topic

The Effect of Model Misspecification on Semi-Supervised Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ting Yang ; Johns Hopkins University, Baltimore ; Carey E. Priebe

Semi-supervised classification-training both on labeled and unlabeled observations-can yield improved performance compared to the classifier based on only the labeled observations. Unlabeled observations are always beneficial to classification if the model we assume is correct. However, they may degrade the classifier performance when the model is misspecified. In the classical classification problem setting, many factors affect the semi-supervised performance, including training data, model specification, estimation method, and the classifier itself. For concreteness, we consider maximum likelihood estimation in finite mixture models and the Bayes plug-in classifier, due to their ubiquitousness and tractability. In this specific setting, we examine the effect of model misspecification on semi-supervised classification performance and shed some light on when and why performance degradation occurs.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:33 ,  Issue: 10 )