Anomaly and Novelty detection for robust semi-supervised learning

TL;DR

This paper introduces robust, adaptive discriminant analysis methods for semi-supervised learning that effectively handle label noise, outliers, and unobserved classes, improving classification in challenging data scenarios.

Contribution

It proposes two EM-based classifiers that jointly or iteratively utilize training and test data for robust semi-supervised classification, addressing key issues like noise, outliers, and new classes.

Findings

Enhanced robustness against label noise and outliers.

Effective detection of unobserved classes in test data.

Improved classification accuracy on synthetic and real datasets.

Abstract

Three important issues are often encountered in Supervised and Semi-Supervised Classification: class-memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the afore-mentioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimate using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method.