Abstract
Most existing active learning methods often manually label samples and train models with labeled data in an iterative way. Unfortunately, at the early stage of the experiment, few labeled data are available, hence, selecting the most valuable data points to label is necessary and important. To this end, we propose a novel method, called Multi-view Representative and Informative-induced Active Learning ( MRI-AL ), which selects samples of both representativeness and informativeness with the help of complementarity of multiple views. Specifically, subspace reconstruction with structure sparsity technique is employed to ensure the selected samples to be representative, while the global similarity constraint guarantees the informativeness of the selected samples. The proposed method is solved efficiently by alternating direction method of multipliers (ADMM). We empirically show that our method outperforms existing early experimental design approaches.
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Acknowledgments
This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.
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Huang, H., Zhang, C., Hu, Q., Zhu, P. (2016). Multi-view Representative and Informative Induced Active Learning. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_12
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