Abstract
This study is an attempt to draw on research of semi-supervised dimension reduction. Many real world problems can be formulated as semi-supervised problems since the data labeling is much more challenging to obtain than the unlabeled data. Dimension reduction benefits the computation performance and is usually applied in the problem with high dimensional data. This paper proposes a semi-supervised dimension reduction achieved with the kernel sliced inverse regression (KSIR). The prior information is applied to estimate the statistical parameters in the KSIR formula. The semi-supervised KSIR performs comparably to other established methods but much more efficient.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chang, C.-C., Pao, H.-K., Lee, Y.-J.: An rsvm based two-teachers–one-student semi-supervised learning algorithm. Neural Networks 25, 57–69 (2012)
Chapelle, O., Zien, A.: Semi-supervised classification by low density separation (2004)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Huang, C.-M., Lee, Y.-J., Lin, D., Huang, S.-Y.: Model selection for support vector machines via uniform design. Computational Statistics & Data Analysis 52(1), 335–346 (2007)
Jolliffe, I.: Principal component analysis. Wiley Online Library (2005)
Lee, Y.-J., Mangasarian, O.: SSVM: A smooth support vector machine for classification. Computational Optimization and Applications 20(1), 5–22 (2001)
Lee, Y.-J., Mangasarian, O.L.: Rsvm: Reduced support vector machines. In: Proceedings of the first SIAM International Conference on Data Mining, pp. 5–7. SIAM (2001)
Li, K.-C.: Sliced inverse regression for dimension reduction. Journal of the American Statistical Association 86(414), 316–327 (1991)
Su, K.-Y.: Kernel sliced inverse regression (ksir) for semi-supervised learning. Master thesis, NTUST (2014)
Tang, W., Zhong, S.: Pairwise constraints-guided dimensionality reduction. In: SDM Workshop on Feature Selection for Data Mining (2006)
Wu, H.-M.: Kernel sliced inverse regression with applications to classification. Journal of Computational and Graphical Statistics 17(3) (2008)
Yeh, Y.-R., Huang, S.-Y., Lee, Y.-J.: Nonlinear dimension reduction with kernel sliced inverse regression. IEEE Transactions on Knowledge and Data Engineering 21(11), 1590–1603 (2009)
Zhang, D., Zhou, Z.-H., Chen, S.: Semi-supervised dimensionality reduction. In: SDM, pp. 629–634. SIAM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, CC., Su, KY. (2014). Semi-supervised Dimension Reduction with Kernel Sliced Inverse Regression. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-13987-6_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
eBook Packages: Computer ScienceComputer Science (R0)