Open Access
MATEC Web of Conferences
Volume 28, 2015
2015 the 4th International Conference on Advances in Mechanics Engineering (ICAME 2015)
Article Number 06003
Number of page(s) 5
Section Computer theory and Application Technology
Published online 28 October 2015
  1. P. Belhumeour, J. Hespanha, and D. Kriegman. Eigenfaces vs. Fisherfaces, “Recognition using class specific linear projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 711–720, 1997. [CrossRef]
  2. M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering”, Advances in Neural Information Processing Systems, volume 15, 2001.
  3. J. Chen, J. Ye, and Q. Li, “Integrating global and local structures: a least squares framework for dimensionality reduction”, Proceedings of 24th International Conference on Machine Learning, 2007.
  4. V. de Silva and J. Tenenbaum, “Global versus local methods in nonlinear dimensionality reduction”, Advances in Neural Information Processing Systems, pages 705–712, 2002.
  5. R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley Interscience, 2nd edition, 2000.
  6. S. Dudoit, J. Fridlyand, and T. P. Speed, “Comparison of discrimination methods for the classification of tumors using gene expression data”, Journal of the American Statistical Association, 97(457):77–87, 2002. [CrossRef]
  7. R. Fisher, “The use of multiple measurements in taxonomic problems”, Annals of Eugenics, 7:179–188, 1936. [CrossRef]
  8. K. Fukunaga, Introduction to Statistical Pattern Classification. Academic Press, San Diego, California, USA, 1990.
  9. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data mining, Inference, and Prediction. Springer, 2001.
  10. X. He and P. Niyogi, “Locality preserving projection”, Advances in Neural Information Processing Systems, 2003.
  11. S. Roweis and L. Saul, “Nonlinear dimensionality reduction by locally linear embedding”, Science, 290(5500): 2323–2326, 2000. [CrossRef] [PubMed]
  12. Y. Song, “A New Parameterized Algorithm for Rapid Peptide Sequencing”, PLoS ONE 9(2): e87476, 2014. [CrossRef]
  13. Y. Song and A. Y. Chi, “A new approach for parameter estimation in the sequence-structure alignment of non-coding RNAs”, Journal of Information Science and Engineering, 2014, in press.
  14. Y. Song, “An improved parameterized algorithm for the independent feedback vertex setprobem”, Theoretical Computer Science, 535(22): 25–30, 2014. [CrossRef]