Abstract
Past work on Linear Dimensionality Reduction (LDR) has emphasized the issues of classification and dimension estimation. However, relatively less attention has been given to the critical issue of eigenvector selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA) without using an effective scheme to select an appropriate set of features/eigenvectors in this space. This paper addresses Linear Dimensionality Reduction through Eigenvector selection for object recognition. It has two main contributions. First, we propose a unified framework for one transform based LDR. Second, we propose a framework for two transform based DLR. As a case study, we consider PCA and Linear Discriminant Analysis (LDA) for the linear transforms. We have tested our proposed frameworks on several public benchmark data sets. Experiments on ORL, UMIST, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition that are based on eigenvector selection.
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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.: Principal Component Analysis. Springer, New York (2002)
Mitra, P., Murthy, C., Pal, S.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 301–312 (2002)
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)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.Z.: Face recognition using laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 27(3), 328–340 (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)
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Dornaika, F., Assoum, A. (2010). Linear Dimensionality Reduction through Eigenvector Selection for Object Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_27
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DOI: https://doi.org/10.1007/978-3-642-17289-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17288-5
Online ISBN: 978-3-642-17289-2
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