Affiliations: Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education Shanghai, Shanghai, China
Abstract: Active learning has proven to be quite effective in a vast array of machine learning tasks. Despite the lower labeling cost of active learning, it has been shown that active learning still can not reach state-of-the-art performance on several classification tasks and is sensitive to initial state. In this work, we propose a novel algorithm to improve the performance of active learning and it’s robustness to initial state. More specifically, we integrate low-rank transformation (LRT) with active learning. In each iteration, LRT is applied to project original high dimensional data to a feature space where data are easier to be classified and then support vector machines classifier is updated in this feature space. As iteration goes on, active learning’s propriety of labeling data improves the performance of LRT, which further promotes the accuracy of SVM classifier. Experiment on several benchmark binary classification datasets results showed the proposed algorithm outperforms other active learning methods in accuracy and robustness.
Keywords: Active learning, low-rank transformation, support vector machines, binary classification
