Abstract
Least squares regression has shown promising performance in the supervised classification. However, conventional least squares regression commonly faces two limitations that severely restrict their effectiveness. Firstly, the strict zero-one label matrix utilized in least squares regression provides limited freedom for classification. Secondly, the modeling process does not fully consider the correlations among samples from the same class. To address the above issues, this paper proposes the inter-class sparsity-based non-negative transition sub-space learning (ICSN-TSL) method. Our approach exploits a transition sub-space to bridge the raw image space and the label space. By learning two distinct transformation matrices, we obtain a low-dimensional representation of the data while ensuring model flexibility. Additionally, an inter-class sparsity term is introduced to learn a more discriminative projection matrix. Experimental results on image databases demonstrate the superiority of ICSN-TSL over existing methods in terms of recognition rate. The proposed ICSN-TSL achieves a recognition rate of up to 98% in normal cases. Notably, it also achieves a classification accuracy of over 87% even on artificially corrupted images.
This work was supported in part by the Natural Science Foundation of China under Grant No. 62106052 and Grant No. 62072118, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant No. 2021B1515120010, in part by the Huangpu International Sci &Tech Cooperation Fundation of Guangzhou, China, under Grant No.2021GH12.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, Z., Wu, X.J., Cai, Y.H., Kittler, J.: Sparse non-negative transition subspace learning for image classification. Signal Process. 183, 107988 (2021)
Chen, Z., Wu, X.J., Kittler, J.: Fisher discriminative least squares regression for image classification. arXiv preprint arXiv:1903.07833 (2019)
Chen, Z., Wu, X.J., Kittler, J.: Low-rank discriminative least squares regression for image classification. Signal Process. 173, 107485 (2020)
Fang, X., et al.: Approximate low-rank projection learning for feature extraction. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5228–5241 (2018)
Georghiades, A.S., Belhumeur, P.N.: Illumination cone models for faces recognition under variable lighting. In: Proceedings of CVPR 1998 (1998)
Han, N., et al.: Double relaxed regression for image classification. IEEE Trans. Circuits Syst. Video Technol. 30(2), 307–319 (2019)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Martinez, A., Benavente, R.: The AR face database: CVC technical report, 24 (1998)
Meng, M., Lan, M., Yu, J., Wu, J., Tao, D.: Constrained discriminative projection learning for image classification. IEEE Trans. Image Process. 29, 186–198 (2019)
Nene, S.A., Nayar, S.K., Murase, H., et al.: Columbia object image library (COIL-20) (1996)
Peng, Y., Zhang, L., Liu, S., Wang, X., Guo, M.: Kernel negative \(\varepsilon \) dragging linear regression for pattern classification. Complexity 2017 (2017)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc.: Ser. B (Methodol.) 58(1), 267–288 (1996)
Wen, J., Xu, Y., Li, Z., Ma, Z., Xu, Y.: Inter-class sparsity based discriminative least square regression. Neural Netw. 102, 36–47 (2018)
Wold, S., Ruhe, A., Wold, H., Dunn, Iii, W.: The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J. Sci. Stat. Comput. 5(3), 735–743 (1984)
Xiang, S., Nie, F., Meng, G., Pan, C., Zhang, C.: Discriminative least squares regression for multiclass classification and feature selection. IEEE Trans. Neural Netw. Learn. Syst. 23(11), 1738–1754 (2012)
Zhang, X.Y., Wang, L., Xiang, S., Liu, C.L.: Retargeted least squares regression algorithm. IEEE Trans Neural Netw. Learn. Syst. 26(9), 2206–2213 (2014)
Zhang, Z., Lai, Z., Xu, Y., Shao, L., Wu, J., Xie, G.S.: Discriminative elastic-net regularized linear regression. IEEE Trans. Image Process. 26(3), 1466–1481 (2017)
Zhao, S., Wu, J., Zhang, B., Fei, L.: Low-rank inter-class sparsity based semi-flexible target least squares regression for feature representation. Pattern Recogn. 123, 108346 (2022)
Zhao, S., Zhang, B., Li, S.: Discriminant and sparsity based least squares regression with L1 regularization for feature representation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1504–1508. IEEE (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, M., Zhao, S., Wu, J., Ma, S. (2024). Inter-class Sparsity Based Non-negative Transition Sub-space Learning. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14427. Springer, Singapore. https://doi.org/10.1007/978-981-99-8435-0_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-8435-0_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8434-3
Online ISBN: 978-981-99-8435-0
eBook Packages: Computer ScienceComputer Science (R0)