A probabilistic approach towards an unbiased semi-supervised cluster tree

Conventionally, it is a prerequisite to acquire a good number of annotated data to train an accurate classifier. However, the acquisition of such dataset is usually infeasible due to the high annotation cost. Therefore, semi-supervised learning has emerged and attracts increasing research efforts in recent years. Essentially, semi-supervised learning is sensitive to the manner how the unlabeled data is sampled. However, the model performance might be seriously deteriorated if biased unlabeled data is sampled at the early stage. In this paper, an unbiased semi-supervised cluster tree is proposed which is learnt using only very few labeled data. Specifically, a K-means algorithm is adopted to build each level of this hierarchical tree in a decent top-down manner. The number of clusters is determined by the number of classes contained in the labeled data. The confidence error of the cluster tree is theoretically analyzed which is then used to prune the tree. Empirical studies on several datasets have demonstrated that the proposed semi-supervised cluster tree is superior to the state-of-the-art semi-supervised learning algorithms with respect to classification accuracy.