Combining Semi-supervised Clustering and Classification Under a Generalized Framework

Abstract

Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from both labeled and unlabeled data has become a hot topic of interest. Inspired by the co-training algorithm, we present a learning framework called CSCC, which combines semi-supervised clustering and classification to learn from both labeled and unlabeled data. Unlike existing co-training style methods that construct diverse classifiers to learn from each other, CSCC leverages the diversity between semi-supervised clustering and classification models to achieve mutual enhancement. Existing classification algorithms can be easily adapted to CSCC, allowing them to generalize from a few labeled data. Especially, in order to bridge the gap between class information and clustering, we propose a semi-supervised hierarchical clustering algorithm that utilizes labeled data to guide the process of cluster-splitting. Within the CSCC framework, we introduce two loss functions to supervise the iterative updating of the semi-supervised clustering and classification models, respectively. Extensive experiments conducted on a variety of benchmark datasets validate the superiority of CSCC over other state-of-the-art methods.