Cited By
View all- Yu GFeng LYao GWang J(2016)Semi-supervised classification using multiple clusteringsPattern Recognition and Image Analysis10.1134/S105466181604021026:4(681-687)Online publication date: 1-Oct-2016
Mixture graph based semi-supervised dimensionality reduction
Authors: 
G. X. Yu, H. Peng, J. Wei, Q. L. MaAuthors Info & Claims
Pages 536 - 541
Abstract
Graph structure is crucial to graph based dimensionality reduction. A mixture graph based semi-supervised dimensionality reduction (MGSSDR) method with pairwise constraints is proposed. MGSSDR first constructs multiple diverse graphs on different random subspaces of dataset, then it combines these graphs into a mixture graph and does dimensionality reduction on this mixture graph. MGSSDR can preserve the pairwise constraints and local structure of samples in the reduced subspace. Meanwhile, it is robust to noise and neighborhood size. Experimental results on facial images feature extraction demonstrate its effectiveness.
References
[1]
L. J. P. van der Maaten, E. O. Postma, and H. J. van den Herik, Dimensionality Reduction: a Comparative Review. Tilburg University Tech. Rep., TiCC-TR 2009- 005, 2009.
[2]
H. Cevikalp, J. Verbeek, F. Jurie, and A. Klaser, "Semi-supervised Dimensionality Reduction Using Pairwise Equivalence Constraints," in Proc. Int. Conf. on Computer Vision Theory and Applications (Funchal, Madeira, Portugal, 2008), pp. 489-496.
[3]
X. J. Zhu, Semi-Supervised Learning Literature, Tech. Rep. 1530, Department of Computer Sciences, University of Wisconsin-Madison, 2008, available: http:// www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf.
[4]
D. Q. Zhang, Z. H. Zhou, and S. C. Chen, "Semi-supervised Dimensionality Reduction," in Proc. SIAM Int. Conf. on Data Mining (Minneapolis, MN, USA, 2007), pp. 629-634.
[5]
J. Wei and H. Peng, Neighborhood Preserving Based Semi_Supervised Dimensionality Reduction, Electron Lett. 19(11), 1190-1191 (2008).
[6]
S. T. Roweis and L. K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science 290 (22), 2323-2326. (2000).
[7]
S. C. Yan, D. Xu, B. Y. Zhang, H. J. Zhang, Q. Yang, and S. Lin, Graph Embedding and Extensions: a General Framework for Dimensionality Reduction, IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40-51 (2007).
[8]
L. Parsons, Q. Hua, and H. Liu, "Subspace Clustering for High Dimensional Data: a Review," in Proc. ACM Int. Conf. on Knowledge Discovery and Data Mining Explorations Newsletter (Seattles, WA, USA, 2004), pp. 90-105.
[9]
T. K. Ho, The Random Subspace Method for Constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832-844 (1998).
[10]
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711-720 (1997).
[11]
M. Belkin and P. Niyogi, Semi-supervised learning on Riemannian manifolds, Mach. Learn. 56(1), 209-239 (2004).
[12]
A. M. Martinez and R. Benavente, The AR-Face Data-Base, CVC Tech. Rep. 24 (1998).
[13]
A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose, IEEE Trans. Pattern Anal. Mach. Intell. 23(6) 643-660 (2001).
- Mixture graph based semi-supervised dimensionality reduction
Recommendations
A unified framework for semi-supervised dimensionality reduction
In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data. Under ...
Semi-supervised dimensionality reduction in image feature space
SAC '08: Proceedings of the 2008 ACM symposium on Applied computingImage feature space is typically complex due to the high dimensionality of data. Effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. In this paper, we propose a semi-...
Enhanced locality preserving projections using robust path based similarity
Curse of dimensionality is a bothering problem in high dimensional data analysis. To enhance the performances of classification or clustering on these data, their dimensionalities should be reduced beforehand. Locality Preserving Projections (LPP) is a ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Copyright © 2010 Pleiades Publishing, Ltd.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 December 2010
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Jan 2025
Other Metrics
Citations
Cited By
View all- Yu GFeng LYao GWang J(2016)Semi-supervised classification using multiple clusteringsPattern Recognition and Image Analysis10.1134/S105466181604021026:4(681-687)Online publication date: 1-Oct-2016