Abstract
Feature extraction is an important way to improve the performance of image recognition. Compared to most of feature extraction methods, the 2-D principal component analysis (2-DPCA) better preserves the structural information of images since it is unnecessary to transform the small size image matrices into high dimensional vectors during the calculation process. In order to improve the robustness of 2-DPCA, nuclear norm-based 2-DPCA (N-2-DPCA) was proposed using nuclear norm as matrix distance measurement. However, 2-DPCA and N-2-DPCA lack the function of sparse feature extraction and selection. Thus, in this paper, we extend N-2-DPCA to sparse case, which is called SN-2-DPCA, for sparse subspace learning. To efficiently solve the model, an alternatively iterative algorithm will also be presented. The proposed SN-2-DPCA would be compared with some advanced 1-D and 2-D feature extraction methods using four well-known data sets. Experimental results indicate the competitive advantage of SN-2-DPCA.
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
References
Hong, Z.Q.: Algebraic feature extraction of image for recognition. Pattern Recognit. 24, 211–219 (1991)
You, J., Li, W., Zhang, D.: Hierarchical palmprint identification via multiple feature extraction. Pattern Recognit. 35, 847–859 (2002)
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010)
Jolliffe, I.T.: Principal Component Analysis, vol. 87, pp. 41–64. Springer, Berlin (2010)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2009)
Noushath, S., Kumar, G.H., Shivakumara, P.: (2D) 2 LDA: an efficient approach for face recognition. Pattern Recognit. 39, 1396–1400 (2006)
Zheng, W.S., Lai, J.H., Li, S.Z.: 1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based? Pattern Recognit. 41, 2156–2172 (2008)
Chen, S., Zhao, H., Kong, M., Luo, B.: 2D-LPP: A two-dimensional extension of locality preserving projections. Neurocomputing 70, 912–921 (2007)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004)
Yang, J., Liu, C.: Horizontal and Vertical 2DPCA-based discriminant analysis for face verification on a large-scale database. IEEE Trans. Inf. Forensics Secur. 2, 781–792 (2008)
Kong, H., Li, X., Wang, L., Teoh, E.K., Wang, J.G., Venkateswarlu, R.: Generalized 2D principal component analysis. In: Proceedings of the IJCNN 2005 (2005)
Zhou, Z., Jin, Z.: Double nuclear norm-based robust principal component analysis for image disocclusion and object detection. Neurocomputing 205, 481–489 (2016)
Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9, 717–772 (2008)
Zhang, F., Yang, J., Tai, Y., Tang, J.: Double nuclear norm-based matrix decomposition for occluded image recovery and background modeling. Image Process. IEEE Trans. 24, 1956–1966 (2015)
He, R., Sun, Z., Tan, T., Zheng, W.S.: Recovery of corrupted low-rank matrices via half-quadratic based nonconvex minimization. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2011)
Fazel, M., Hindi, H., Boyd, S.P.: A rank minimization heuristic with application to minimum order system approximation. In: Proceedings of the 2001 American Control Conference (2001)
Fornasier, M., Rauhut, H., Ward, R.: Low-rank matrix recovery via iteratively reweighted least squares minimization. SIAM J. Optim. 21, 1614–1640 (2011)
Qian, J., Luo, L., Yang, J., Zhang, F., Lin, Z.: Robust nuclear norm regularized regression for face recognition with occlusion. Pattern Recognit. 48, 3145–3159 (2015)
Zhang, F., Yang, J., Qian, J., Xu, Y.: Nuclear norm-based 2-DPCA for extracting features from images. IEEE Trans. Neural Networks Learn. Syst. 26, 2247–2260 (2015)
Lai, Z., Wong, W.K., Xu, Y., Yang, J., Zhang, D.: Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans. Neural Networks Learn. Syst. 27, 1–13 (2015)
Lai, Z., Wan, M., Jin, Z., Yang, J.: Sparse two-dimensional local discriminant projections for feature extraction. Neurocomputing 74, 629–637 (2011)
Yin, F., Jiao, L.C., Shang, F., Wang, S., Hou, B.: Fast fisher sparsity preserving projections. Neural Comput. Appl. 23, 691–705 (2012)
Zou, H., Hastie, T., Tibshirani, R.: Sparse Principal Component Analysis. J. Comput. Graph. Stat. 15, 265–286 (2014)
Zhou, T., Tao, D., Wu, X.: Manifold elastic net: a unified framework for sparse dimension reduction. Data Min. Knowl. Discov. 22, 340–371 (2011)
Li, X., Pang, Y., Yuan, Y.: L1-norm-based 2DPCA. IEEE Trans. Syst. Man Cybern. Part B Cybern. A Publ. IEEE Syst. Man Cybern. Soc. 40, 1170–1175 (2010)
Martinez, A.M.: The AR face database. CVC Technical report 24 (1998)
Jonathon Phillips, P., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Chen, Y., Lai, Z., Zhang, Y. (2016). Sparse Nuclear Norm Two Dimensional Principal Component Analysis. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-46654-5_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46653-8
Online ISBN: 978-3-319-46654-5
eBook Packages: Computer ScienceComputer Science (R0)