Abstract
As we know, sparse representation methods can achieve high accuracy for classification of high-dimensional data. However, they usually show poor performance in performing classification of low-dimensional data. In this paper, the increase of the sample dimension for sparse representation is studied and surprising accuracy improvement is obtained. The paper has the following main value. First, the designed method obtains promising results for classification of low-dimensional data and is very useful for widening the applicability of sparse representation. The accuracy of the designed method may be 10% higher than that of sparse representation based on original samples. To our knowledge, no similar work is available. Second, the designed method is simple and has a low computational cost. Extensive experiments are conducted and the experimental results also show that the designed method can be applied to improve other methods too.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Wright, J., Ma, Y., Mairal, J., et al.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)
Xu, Y., Zhang, D., Yang, J., Yang, J.-Y.: A two-phase test sample sparse representation method for use with face recognition. IEEE Trans. Circuits Syst. Video Technol. 21(9), 1255–1262 (2011)
Zhang, L., et al.: Sparse representation or collaborative representation: which helps face recognition. Proc. Int. Congr. Comput. Vision (2011)
Kroeker, K.L.: Face recognition breakthrough. Commun. ACM 52(8), 18–19. https://doi.org/10.1145/1536616.1536623
Zhang, E., Zhang, X., Liu, H., Jiao, L.: Fast multifeature joint sparse representation for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 12(7), 1397–1401 (2015)
Yang, S., Lv, Y., Ren, Y., Yang, L., Jiao, L.: Unsupervised images segmentation via incremental dictionary learning based sparse representation. Inf. Sci. 269, 48–59 (2014)
Li, X.: Image recovery via hybrid sparse representation: a deterministic annealing approach. IEEE J. Sel. Top. Signal Process. 5(5), 953–962 (2011). Special issue on adaptive sparse representation
Xu, Y., Zhang, B., Zhong, Z.: Multiple representations and sparse representation for image classification. Pattern Recognit. Lett. (2015)
Gao, S., Tsang, I.W.-H., Chia, L.-T.: Kernel sparse representation for image classification and face recognition. In: Lecture Notes in Computer Science, vol. 6314, pp. 1–14 (2010)
Yin, J., Liu, Z., Jin, Z., Yang, W.: Kernel sparse representation based classification. Neurocomputing 77(1), 120–128 (2012)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New Jersey (2004)
Xu, Y., Fan, Z., Zhu, Q.: Feature space-based human face image representation and recognition. Opt. Eng. 51(1), 017205 (2012)
Chen, Z., Zuo, W., Qinghua, H., Lin, L.: Kernel sparse representation for time series classification. Inf. Sci. 292, 15–26 (2015)
Zhang, L., Zhou, W., Li, F.-Z.: Kernel sparse representation-based classifier ensemble for face recognition. Multimed. Tools Appl. 74(1), 123–137 (2015)
Ripley, B.D.: Pattern Recognition and Neural Networks, 1st edn, p. 416. Springer, Cambridge (2007)
Xu, Y., Li, X., Yang, J., Lai, Z., Zhang, D.: Integrating conventional and inverse representation for face recognition. IEEE Trans. Cybern. 44(10), 1738–1746 (2014)
Xu, Y., Zhang, D., Jin, Z., Li, M., Yang, J.-Y.: A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recogn. 39(6), 1026–1033 (2006)
Xu, Y., Zhu, Q., Fan, Z., Zhang, D., Mi, J., Lai, Z.: Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf. Sci. 238(20), 138–148 (2013)
Acknowledgements
This work was supported by the Joint Fund of Department of Science and Technology of Guizhou Province and Guizhou University under grant: LH [2014]7635, Research Foundation for Advanced Talents of Guizhou University under grant: (2016) No. 49, Key Supported Disciplines of Guizhou Province—Computer Application Technology (No. QianXueWeiHeZi ZDXX[2016]20), Specialized Fund for Science and Technology Platform and Talent Team Project of Guizhou Province (No. QianKeHePingTaiRenCai [2016]5609), and the work was also supported by National Natural Science Foundation of China (61462013, 61661010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Q., Zhang, Y., Xiao, L., Li, Y. (2019). Extension of Sample Dimension and Sparse Representation Based Classification of Low-Dimension Data. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_66
Download citation
DOI: https://doi.org/10.1007/978-981-13-5841-8_66
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5840-1
Online ISBN: 978-981-13-5841-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)