A Novel Active Learning Algorithm for Robust Image Classification

Training samples need to be labeled before being used to train classification model, which usually takes too much labor and material resources. Recently, this problem has attracted widespread attention. In order to reduce the workload of labeling samples, we propose a novel active learning methodology, which uses locally linear reconstruction coefficients to construct semi-supervised data manifold adaptive kernel space. Comparing the new method with other sampling approaches on several real-world image datasets, experimental results indicate that the novel algorithm has preferable classification ability. Especially, it can show higher classification accuracy under the condition that only a few samples are selected to train the classifier model.