Abstract
Recently, a graph-based method was proposed for Linear Dimensionality Reduction (LDR). It is based on Locality Preserving Projections (LPP). It has been successfully applied in many practical problems such as face recognition. In order to solve the Small Size Problem that usually affects face recognition, LPP is preceded by a Principal Component Analysis (PCA). This paper has two main contributions. First, we propose a recognition scheme based on the concatenation of the features provided by PCA and LPP. We show that this concatenation can improve the recognition performance. Second, we propose a feasible approach to the problem of selecting the best features in this mapped space. We have tested our proposed framework on several public benchmark data sets. Experiments on ORL, UMIST, PF01, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition.
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
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Borg, I., Groenen, P.: Modern Multidimensional Scaling: theory and applications. Springer, New York (2005)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2319–2327 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Martinez, A.M., Zhu, M.: Where are linear feature extraction methods applicable? IEEE Trans. Pattern Analysis and Machine Intelligence 27(12), 1934–1944 (2005)
Suna, Z., Bebisa, G., Miller, R.: Object detection using feature subset selection. Pattern Recognition 37, 2165–2176 (2004)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extension: a general framework for dimensionality reduction. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)
He, X., Niyogi, P.: Locality preserving projections. In: Conference on Advances in Neural Information Processing Systems (2003)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intelligence 27(3), 328–340 (2005)
Zhang, L., Qiao, L., Chen, S.: Graph-optimized locality preserving projections. Pattern Recognition 43, 1993–2002 (2010)
Yu, W., Teng, X., Liu, C.: Face recognition using discriminant locality preserving projections. Image and Vision Computing 24, 239–248 (2006)
Mitra, P., Murthy, C., Pal, S.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 301–312 (2002)
Liua, H., Suna, J., Liua, L., Zhang, H.: Feature selectionwith dynamic mutual information. Pattern Recognition 43, 1330–1339 (2009)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge Data Engineering 17, 494–502 (2005)
Wu, X., Kumar, V., et al.: Top 10 algorithms in data mining. Knowledge Information Systems 14(1), 1–37 (2008)
Srinivas, M., Patnaik, L.: Genetic algorithms: a survey. IEEE Computer 27(6), 17–26 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dornaika, F., Assoum, A., Bosaghzadeh, A. (2011). Combining Linear Dimensionality Reduction and Locality Preserving Projections with Feature Selection for Recognition Tasks. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-23687-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
eBook Packages: Computer ScienceComputer Science (R0)