Combining Active Learning and Semi-supervised Learning Using Local and Global Consistency

Abstract

Semi-supervised learning and active learning are important techniques to solve the shortage of labeled examples. In this paper, a novel active learning algorithm combining semi-supervised Learning with Local and Global Consistency (LLGC) is proposed. It selects the example that can minimize the estimated expected classification risk for labeling. Then, a better classifier can be trained with labeled data and unlabeled data using LLGC. The experiments on two datasets demonstrate the effectiveness of the proposed algorithm.KeywordsActive learningsemi-supervised learningimage classification