Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
This work provides theoretical generalization guarantees for semi-supervised multiclass classification, which is an incremental advancement over existing binary case results.
The authors developed Rademacher complexity bounds for a two-step semi-supervised multiclass classification algorithm that partitions data and identifies clusters with predominant classes, then trains a classifier with a penalization term. Their theoretical bounds extend binary case results to multiclass problems, and experiments on various datasets provide empirical support.
We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $κ$ predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the $κ$ predominant classes of the identified clusters. The resulting data-dependent generalization error bound involves the margin distribution of the classifier, the stability of the clustering technique used in the first step and Rademacher complexity terms corresponding to partially labeled training data. Our theoretical result exhibit convergence rates extending those proposed in the literature for the binary case, and experimental results on different multiclass classification problems show empirical evidence that supports the theory.