Preserving Global and Local Structures for Supervised Dimensionality Reduction

Open Access

Issue		MATEC Web of Conferences Volume 28, 2015 2015 the 4^th International Conference on Advances in Mechanics Engineering (ICAME 2015)


Article Number		06003
Number of page(s)		5
Section		Computer theory and Application Technology
DOI		https://doi.org/10.1051/matecconf/20152806003
Published online		28 October 2015

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. [Google Scholar]
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering”, Advances in Neural Information Processing Systems, volume 15, 2001. [Google Scholar]
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. [Google Scholar]
V. de Silva and J. Tenenbaum, “Global versus local methods in nonlinear dimensionality reduction”, Advances in Neural Information Processing Systems, pages 705–712, 2002. [Google Scholar]
R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley Interscience, 2nd edition, 2000. [Google Scholar]
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] [Google Scholar]
R. Fisher, “The use of multiple measurements in taxonomic problems”, Annals of Eugenics, 7:179–188, 1936. [CrossRef] [Google Scholar]
K. Fukunaga, Introduction to Statistical Pattern Classification. Academic Press, San Diego, California, USA, 1990. [Google Scholar]
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data mining, Inference, and Prediction. Springer, 2001. [Google Scholar]
X. He and P. Niyogi, “Locality preserving projection”, Advances in Neural Information Processing Systems, 2003. [Google Scholar]
S. Roweis and L. Saul, “Nonlinear dimensionality reduction by locally linear embedding”, Science, 290(5500): 2323–2326, 2000. [Google Scholar]
Y. Song, “A New Parameterized Algorithm for Rapid Peptide Sequencing”, PLoS ONE 9(2): e87476, 2014. [CrossRef] [Google Scholar]
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. [Google Scholar]
Y. Song, “An improved parameterized algorithm for the independent feedback vertex setprobem”, Theoretical Computer Science, 535(22): 25–30, 2014. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.