Abstract
Linear Laplacian Discrimination (LLD) as a non-linear feature extraction method has obtained very extensive applications. However, LDD suffers from the small sample size problem (SSS) and/or the type of the sample space when it is used. In order to circumvent such shortcomings, in this paper a Laplacian Maximum Scatter Difference Discriminant Criterion (LMSDC) is proposed by using new contextual-distance metric and integrating maximum scatter difference discriminant criterion(MSDC) into LDD. The proposed criterion can obviously decrease the dependence on the sample space and solve small sample size problem. The experimental results indicate the above advantages of the proposed method LMSDC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jolliffe, T.: Principal component analysis. Springer, New York (1986)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Hsieh, P., Wang, D., Hsu, C.: A linear feature extraction for multiclass classification problems based on class mean and covariance discriminant information. IEEE Trans. Pattern Analysis Machine Intelligence 28(6), 223–235 (2006)
He, X.F., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada (2003)
Zhao, D.L., Lin, Z.C., Xiao, R., Tang, X.O.: Linear laplacian discrimination for feature extraction. In: IEEE Computer Society Conference on Computer Vision And Pattern Recognition (CVPR), Minnesota, USA (2007)
Zhang, W., Lin, Z.C., Tang, X.O.: Tensor linear laplacian discrimination (TLLD) for feature extraction. Pattern Recognition 42(9), 1941–1948 (2009)
Zhao, D.L., Lin, Z.C., Xiao, R., Tang, X.O.: Laplacian PCA and its applications. In: IEEE 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil (2007)
Zhao, D.L., Lin, Z.C., Xiao, R., Tang, X.O.: Contextual distance for data perception. In: IEEE 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil (2007)
Ma, Y., Derksen, H., Hong, W., Wright, J.: Segmentation of multivariate mixed data via lossy data coding and compression. IEEE Trans. Pattern Analysis Machine Intelligence 29(9), 1546–1562 (2007)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Irvine, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, J., Xiang, L. (2011). Laplacian Maximum Scatter Difference Discriminant Criterion. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_88
Download citation
DOI: https://doi.org/10.1007/978-3-642-23214-5_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23213-8
Online ISBN: 978-3-642-23214-5
eBook Packages: Computer ScienceComputer Science (R0)