The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Manifold Discriminant Analysis
Abstract
This paper presents a novel discriminative learning method, called manifold discriminant analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we formulate the problem as classification-oriented multi-manifolds learning. Aiming at maximizing “manifold margin”, MDA seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced. As a result, new testing manifold can be more reliably classified in the learned embedding space. The proposed method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization. Comprehensive comparisons and extensive experiments demonstrate the effectiveness of our method.
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Citations
Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Chung, F. (1996) · CBMS Regional Conference Series in Mathematics · doi:10.1090/cbms/092 · Cited by 5,524
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Belkin, M., & Niyogi, P. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,412
(2006) · Cited by 3,624
Finding Groups in Data: An Introduction to Cluster Analysis.
Sarle, W. S., Kaufman, L., & Rousseeuw, P. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290430 · Cited by 3,539
Face recognition using Laplacianfaces
He, X., Yan, S., Hu, Y., Niyogi, P., & Zhang, H.-J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.55 · Cited by 3,264
Cited by
Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition
Liu, M., Shan, S., Wang, R., & Chen, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.226 · Cited by 392
Self-citation
Metadata
- Authors
- Wang, R., & Chen, X.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 429-436
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2120453412
- Cited by
- 285
- References
- 28
- FWCI
- 6.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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