Jin-fu Yang

Publications (3)1.69 Total impact

• Article: [Studies of spectra classification based on kernel covering algorithm].

  Jin-fu Yang, Xin Xu, Fu-chao Wu, Yong-heng Zhao
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  ABSTRACT: A kernel based covering algorithm, called the kernel covering algorithm (KCA), is proposed for the classification of celestial spectra. This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space. The experiments show that the classification result based on KCA is a little less than that based on SVM. However, KCA only involves the distance computation without the need to solve the quadratic programming problem. Also, KCA is insensitive to the width of gauss window. Although KCA has a comparable classification performance with the covering algorithm, it changes the distance between samples in feature space by the nonlinear mapping such that the distribution of samples is more adaptable to classify. Therefore, the number of KCA's resulting support vectors is significantly smaller than that of the covering algorithm.
  Guang pu xue yu guang pu fen xi = Guang pu 04/2007; 27(3):602-5. · 0.84 Impact Factor
• Article: [Spectra classification based on generalized discriminant analysis].

  Xin Xu, Jin-fu Yang, Fu-chao Wu, Yong-heng Zhao
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  ABSTRACT: A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework. LDA, GDA, PCA and KPCA were experimentally compared with these three different kinds of spectra. Among these four techniques, GDA obtains the best result, followed by LDA, and PCA is the worst. Although KPCA is also a kernel based technique, its performance is not satisfactory if the selected number of the principal components is small, and in some cases, it appears even worse than LDA, a non-kernel based technique.
  Guang pu xue yu guang pu fen xi = Guang pu 11/2006; 26(10):1960-4. · 0.84 Impact Factor
• Conference Proceeding: Automated Spectral Classification of QSOs and Galaxies by Radial Basis Function Network with Dynamic Decay Adjustment.

  Mei-fang Zhao, Jin-fu Yang, Yue Wu, Fu-chao Wu, Ali Luo
  Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II; 01/2006