Active learning via local structure reconstruction

To select the most representative data points for labeling, two typical active learning methods, Transductive Experimental Design (TED) and Robust Representation and Structured Sparsity (RRSS), have been recently proposed. They yield impressive results. However, both of them neglected the local structure of data points which is helpful for selecting representative data points. Therefore, in this paper, we propose a novel active learning method via local structure reconstruction to select representative data points. Specifically, we construct a simple but effective graph to search the local relationship of data points. Then an optimization model is formulated to fulfill the data point reconstruction and select the most representative data points. Furthermore, we define a simple but useful classifier based on a linear regression model for better exploring the potential classification performance of selected data points. Experimental results on two synthetic datasets and two face databases demonstrate the effectiveness of our method and the efficiency of the defined classifier.