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Publications (2)3.22 Total impact

  • Jun-Bao Li, Zhi-Ming Yang, Yang Yu, Zhen Sun
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    ABSTRACT: This paper is to propose semi-supervised kernel learning based optical image recognition, called Semi-supervised Graph-based Global and Local Preserving Projection (SGGLPP) through integrating graph construction with the specific DR process into one unified framework. SGGLPP preserves not only the positive and negative constraints but also the local and global structure of the data in the low dimensional space. In SGGLPP, the intrinsic and cost graphs are constructed using the positive and negative constraints from side-information and k nearest neighbor criterion from unlabeled samples. Moreover, kernel trick is applied to extend SGGLPP called KSGGLPP by on the performance of nonlinear feature extraction. Experiments are implemented on UCI database and two real image databases to testify the feasibility and performance of the proposed algorithm.
    Optics Communications 08/2012; 285(18):3697–3703. · 1.44 Impact Factor
  • Jun-Bao Li, Yang Yu, Zhi-Ming Yang, Lin-Lin Tang
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    ABSTRACT: Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.
    Journal of Medical Systems 07/2011; 36(5):2779-86. · 1.78 Impact Factor