Abstract
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn. Academic Press Professional, Inc., San Diego (1990)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1222–1228 (2004)
Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data – with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 27(1), 4–13 (2005)
Ye, J.: Least squares linear discriminant analysis. In: ICML 2007: Proc. of the 24th Intl. Conf. on Machine Learning, pp. 1087–1093. ACM, New York (2007)
Chandrasekaran, S., Manjunath, B., Wang, Y., Winkler, J., Zhang, H.: An eigenspace update algorithm for image analysis. Graphical Models and Image Processing 59(5), 321–332 (1997)
Ozawa, S., Toh, S.L., Abe, S., Pang, S., Kasabov, N.: Incremental learning of feature space and classifier for face recognition. Neur. Netw. 18(5), 575–584 (2005)
Ye, J., Li, Q., Xiong, H., Park, H., Janardan, R., Kumar, V.: Idr/qr: An incremental dimension reduction algorithm via qr decomposition. IEEE Trans. on Knowl. and Data Eng. 17(9), 1208–1222 (2005)
Kim, T.K., Wong, S.F., Stenger, B., Kittler, J., Cipolla, R.: Incremental linear discriminant analysis using sufficient spanning set approximations. In: Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)
Zhao, H., Yuen, P.C.: Incremental linear discriminant analysis for face recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38(1), 210–221 (2008)
Liu, L.P., Jiang, Y., Zhou, Z.H.: Least square incremental linear discriminant analysis. In: Intl Conf. on Data Mining, ICDM 2009, pp. 298–306 (2009)
Chu, D., Thye, G.S.: A new and fast implementation for null space based linear discriminant analysis. Pattern Recognition 43(4), 1373–1379 (2010)
Ferri, F.J., Diaz-Chito, K., Díaz-Villanueva, W.: Efficient dimensionality reduction on undersampled problems through incremental discriminative common vectors. In: Intl. Conf. on Data Mining Workshops, ICDMW 2010, pp. 1159–1166 (2010)
Lu, G.F., Zou, J., Wang, Y.: Incremental learning of discriminant common vectors for feature extraction. Appl. Math. and Computation 218(22), 11269–11278 (2012)
Golub, G.H., Van Loan, C.F.: Matrix Computations (Johns Hopkins Studies in Mathematical Sciences), 3rd edn. The Johns Hopkins Univ. Press (1996)
Lu, G.F., Zheng, W.: Complexity-reduced implementations of complete and null-space-based linear discriminant analysis. Neural Networks (to appear, 2013)
Tamura, A., Zhao, Q.: Rough common vector: A new approach to face recognition. In: IEEE Intl. Conf. on Syst, Man and Cybernetics, pp. 2366–2371 (2007)
Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 707–720. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferri, F.J., Diaz-Chito, K., Diaz-Villanueva, W. (2013). Fast Approximated Discriminative Common Vectors Using Rank-One SVD Updates. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)