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Semi-supervised multiple kernel intact discriminant space learning for image recognition

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Abstract

In practice, huge amount of samples is readily available, while labeled samples are often very limited and too expensive to be easily obtained. Multi-view features usually reveal different types of traits of labeled and unlabeled samples. Semi-supervised multi-view learning is a learning paradigm designed to meet the requirement of learning from complementary information of multiple views of labeled and unlabeled samples. In this paper, we propose a semi-supervised multiple kernel intact discriminant space learning (SMKIDSL) method to discover latent intact feature representations for those samples. SMKIDSL employs correlation discriminant analysis and label regression to fully use class label information for enhancing the discriminant power of latent intact feature representations. In SMKIDSL, multi-view collaboration learning mechanism is utilized to efficiently integrate complementary information of multiple views, which enables optimal view being dominant in learning process. Besides, kernel technique is used to tackle nonlinear issue of original multi-view features for exploiting more discriminant information. Comprehensive experiments are conducted on Caltech 101, LFW, MNIST and RGB-D datasets. And the experimental results demonstrate the effectiveness and efficiency of our proposed method. The robustness of our method is also confirmed by those results.

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Acknowledgements

This work is partially funded by the National Science Foundation of China (NSFC) with Grant numbers of 61272273 and 61702280. It is also supported by Nanjing University of Posts and Telecommunications under Grant number of XJKY14016, the Scientific Research Staring Foundation for Introduced Talents in NJUPT (NUPTSF, No. NY217009) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX17_0777). In addition, it is also funded by Education Department of Jiangxi Province under the Grant numbers GJJ151066 and GJJ151076. The authors declare that there is no conflict of interest regarding the publication of this paper.

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Authors and Affiliations

  1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Xiwei Dong, Fei Wu & Xiao-Yuan Jing

  2. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Xiao-Yuan Jing

  3. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, China

    Xiwei Dong

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Dong, X., Wu, F. & Jing, XY. Semi-supervised multiple kernel intact discriminant space learning for image recognition. Neural Comput & Applic 31, 5309–5326 (2019). https://doi.org/10.1007/s00521-018-3367-7

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