SCIENCE CHINA Information Sciences, Volume 63 , Issue 1 : 119204(2020) https://doi.org/10.1007/s11432-018-9535-9

Kernel semi-supervised graph embedding model for multimodal and mixmodal data

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  • ReceivedMay 23, 2018
  • AcceptedJun 29, 2018
  • PublishedOct 8, 2019


There is no abstract available for this article.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61673027, 61503375) and Fundamental Research Funds for the Central Universities (Grant Nos. CXTD10-05, 18QD18 in UIBE, DUT19LK18).


[1] Zhang Q, Chu T G. Semi-supervised discriminant analysis based on sparse-coding theory. In: Proceedings of the 35th Chinese Control Conference, 2016. 7082--7087. Google Scholar

[2] Zhang Q, Chu T. Learning in multimodal and mixmodal data: locality preserving discriminant analysis with kernel and sparse representation techniques. Multimed Tools Appl, 2017, 76: 15465-15489 CrossRef Google Scholar

[3] Liu Y, Liao S Z. Kernel selection with spectral perturbation stability of kernel matrix. Sci China Inf Sci, 2014, 57: 1-10 CrossRef Google Scholar

[4] Tao J W, Chung F L, Wang S T. A kernel learning framework for domain adaptation learning. Sci China Inf Sci, 2012, 55: 1983-2007 CrossRef Google Scholar

[5] Mehrkanoon S, Suykens J A K . Scalable semi-supervised kernel spectral learning using random fourier features. In: Proceedings of IEEE Symposium Series on Computational Intelligence, 2017. Google Scholar

[6] Zhang A, Gao X. Data-dependent kernel sparsity preserving projection and its application for semi-supervised classification. Multimed Tools Appl, 2018, 77: 24459-24475 CrossRef Google Scholar

[7] Schölkopf B, Smola A J. Learning with Kernels. Cambridge: MIT Press, 2002. Google Scholar

  • Figure 1

    (Color online) Handwriting recognition. Average recognition error rates obtained by $k$-nn classifier ($k=5$) for (a) USPS-eo, (b) USPS-sl, and (c) USPS-MNIST tasks.