Computer Science ›› 2018, Vol. 45 ›› Issue (6): 251-258.doi: 10.11896/j.issn.1002-137X.2018.06.045

• Artificial Intelligence • Previous Articles     Next Articles

Unsupervised Active Learning Based on Adaptive Sparse Neighbors Reconstruction

LV Ju-jian1,2, ZHAO Hui-min1,2, CHEN Rong-jun1, LI Jian-hong3   

  1. Guangdong Polytechnic Normal University,Guangzhou 510665,China1;
    Key Laboratory of Guangzhou Digital Content Processing and Security Technology,Guangzhou 510665,China2;
    Language Engineering and Computing Laboratory,Guangdong University of Foreign Studies,Guangzhou 510006,China3
  • Received:2017-01-11 Online:2018-06-15 Published:2018-07-24

Abstract: In many information processing tasks,individuals are easy to get a lot of unlabeled data,but labeling the unlabeled data is quite time-consuming and usually expensive.As an important learning method in the field of machine lear-ning,active learning reduces the cost of labeling data by selecting the most information data points to label.However,most of the existing active learning algorithms are supervised method based on the classifier,not suitable for the sample selection problem without any label information.Aiming at this problem,a novel unsupervised active learning algorithm was proposed,called active learning based on adaptive sparse neighbors reconstruction,by learning from the optimal experiment design and combining the adaptive sparse neighbors reconstruction.The proposed algorithm adaptively selects the neighborhood scale according to different regional distribution of dataset,searches the sparse neighbors and calculates the reconstruct coefficients simultaneously,and can choose the most representative data points of the distribution structure of dataset without any label information.Empirical results on both synthetic and real-world data sets show that the proposed algorithm has high performance in classification accuracy and robustness under the same labeling cost.

Key words: Active learning, Local linear reconstruction, Optimal experimental design, Sparse reconstruction, Transductive experimental design

CLC Number: 

  • TP181
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