Batch mode activc learning with hierarchical-structured embedded variance

Abstract

We consider the problem of active learning when the categories are represented as a tree with leaf nodes as outputs and internal nodes as clusters of the outputs at multiple granularity. Recent work has improved the traditional techniques by moving beyond "flat" structure through incorporation of the label hierarchy into the uncertainty measure. However, these methods have two major limitations when used. First, these methods roughly use the information in the label structure but do not take into account the training samples, which may lead to a sampling bias due to their crude approximation of the class relations. Second, none of these methods can work in a batch mode to reduce the computational time of training. We propose a batch mode active learning scheme that exploits both the hierarchical structure of the labels and the characteristics of the training data to select the most informative data for human labeling. We achieve this goal by first using an approach based on graph embedding that embeds the relationships between the labels and data points in a transformed low-dimensional space. Then, we compute uncertainty by calculating the variance among the points and the labels in the embedding space. Finally, the selection criterion is designed to construct batches and incorporate a diversity measure. Experimental results indicate that our technique achieves a notable improvement in performance over the state-of-the-art approaches.