Adaptive Graph Embedding Discriminant Projections
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Graph embedding based learning method plays an increasingly significant role on dimensionality reduction (DR). However, the selection to neighbor parameters of graph is intractable. In this paper, we present a novel DR method called adaptive graph embedding discriminant projections (AGEDP). Compared with most existing DR methods based on graph embedding, such as marginal Fisher analysis which usually predefines the intraclass and interclass neighbor parameters, AGEDP applies all the homogeneous samples for constructing the intrinsic graph, and simultaneously selects heterogeneous samples within the neighborhood generated by the farthest homogeneous sample for constructing the penalty graph. Therefore, AGEDP not only greatly enhances the intraclass compactness and interclass separability, but also adaptively performs neighbor parameter selection which considers the fact that local manifold structure of each sample is generally different. Experiments on AR and COIL-20 datasets demonstrate the effectiveness of the proposed method for face recognition and object categorization, and especially under the interference of occlusion, noise and poses, it is superior to other graph embedding based methods with three different classifiers: nearest neighbor classifier, sparse representation classifier and linear regression classifier.
KeywordsGraph embedding Dimensionality reduction Neighbor parameter selection Discriminant projection Face recognition Object categorization
This work was supported by the National Natural Science Foundation of China (Nos. 61071137, 61071138, 61027004), and the 973 Program of China (Project No. 2010CB327900). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper.
- 6.He X, Niyogi P (2003) Locality preserving projections. In: Advances in neural information processing system (NIPS) 16:100–115Google Scholar
- 10.Sun SL (2013) Tangent space intrinsic manifold regularization for data representation. In: Proceedings of the 1st IEEE China summit and international conference on signal and information processing: 179–183Google Scholar
- 11.He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. IEEE international conference on computer vision (ICCV): 1208–1213Google Scholar
- 17.Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in neural information processing systems 14Google Scholar
- 18.Nene SA, Nayar SK, Murase H (1996) Columbia object image library (COIL-20). Technical, Report CUCS-005-96Google Scholar
- 21.Conover WJ (1999) Practical nonparametric statistics. Wiley, New YorkGoogle Scholar
- 22.Diethe T, Hardoon DR, Shawe-Taylor J (2008) Multiview Fisher discriminant analysis. NIPS Workshop Learning from Multiple SourcesGoogle Scholar
- 23.Chen QN, Sun SL (2009) Hierarchical multi-view Fisher discriminant analysis. Lecture notes in computer science, vol 5864. Springer, Berlin, pp 289–298Google Scholar