ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (1): 63-70.doi: 10.7544/issn1000-1239.2017.20150796

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Fast Kernel-Based Partial Label Learning Algorithm Based on Variational Gaussian Process Model

Zhou Yu1, He Jianjun1,2, Gu Hong1   

  1. 1(Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024); 2(College of Information and Communication Engineering, Dalian Minzu University, Dalian, Liaoning 116600)
  • Online:2017-01-01

Abstract: Partial label learning is a weakly-supervised machine learning framework proposed recently. Since it loosens the requirement to training data set, i.e. the learning model can be obtained when each training example is only associated with a candidate set of the ground-truth labels, and partial label learning framework can be used to deal with many real-world tasks more conveniently. The ambiguity in training data inevitably makes partial label learning problem more difficult to be addressed than traditional classification problem, and only several algorithms for small-scale training set are available up to the present. Based on ECOC technology and variational Gaussian process model, this paper presents a fast kernel-based partial label learning algorithm which can deal with large-scale problem effectively. The basic strategy is to convert the original training data set into several standard two-class data sets by using ECOC technology firstly, and then to develop a binary classify with lower computational complexity on each two-class data set by using variational Gaussian process model. The experimental results show that the proposed algorithm can achieve almost the same accuracy as the existing state-of-the-art kernel-based partial label learning algorithms but use shorter computing time. More specifically, the proposed algorithm can deal with the problems with millions samples within 40 minutes on a personal computer.

Key words: partial label learning, kernel method, large-scale data, Gaussian process, classification

CLC Number: