Online semi-supervised learning with learning vector quantization

Abstract

Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning, in which the data appear in a sequential manner with a mixture of both labeled and unlabeled samples. Despite the recent advances, there are still many unsolved problems in this area. In this paper, we propose a novel OSSL method based on learning vector quantization (LVQ). LVQ classifiers, which represent the data of each class by a set of prototypes, have found their usage in a wide range of pattern recognition problems and can be naturally adapted to the online scenario by updating the prototypes with stochastic gradient optimization. However, most of the existing LVQ algorithms were designed for supervised classification. To extract useful information from unlabeled data, we propose two simple and computationally efficient methods based on clustering assumption. To be specific, we use the maximum conditional likelihood criterion for updating prototypes when data sample is labeled, and the Gaussian mixture clustering criterion or neural gas clustering criterion for adjusting prototypes when data sample is unlabeled. These two criteria are utilized alternatively according to the availability of label information to make full use of both supervised and unsupervised data to boost the performance. By extensive experiments, we show that the proposed method exhibits higher accuracy compared with the baseline methods and graph-based methods and is much more efficient than graph-based methods in both training and test time.