Abstract
Reducing the dimensionality of image with high-dimensional feature plays a significant role in image retrieval and classification. Recently, two methods have been proposed to improve the efficiency and accuracy of dimensionality reduction, one uses CUR matrix decompositions to construct low rank matrix approximations and another approach for dimension reduction trains an auto-encoder with deep architecture to learn low-dimensional codes. In this paper, after above two mentioned methods are respectively utilized to reduce the high-dimensional features of images, we train individual classifiers on both original and reduced feature space for image classification. This paper compares these two approaches with other approaches in image classification. At the same, we also study the effects of the depth of layers on the performance of dimensionality reduction using auto-encoder.
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References
Hyvrinen, A.: Survey on independent component analysis. Neural Computing Surveys 2, 94–128 (1999)
Wold, S.: Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2(1-3), 37–52 (1987)
Mika, S., Scholkopf, B., Olkopf, B.S., Smola, A., Muller, K.-R., Scholz, M., Ratsch, G.: Kernel pca and de-noising in feature spaces (1999)
Chatfield, C., Collins, A.J.: Introduction to Multivariate Analysis. Chapman and Hall, Boca Raton (1980)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Langford, J.C., Tenenbaum, J.B., de Silva, V.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)
He, X., Niyogi, P.: Locality preserving projections (2002)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Drineas, P., Mahoney, M.W., Muthukrishnan, S.: Relative-error cur matrix decompositions. SIAM J. Matrix Anal. Appl. 30(2), 844–881 (2008)
Mahoneya, M.W., Drineas, P.: Cur matrix decompositions for improved data analysis. PNAS 106(3), 697–702 (2009)
Buciu, I., Nikolaidis, N., Pitas, I.: Non-negative matrix factorization in polynomial feature space. Technical report, Department of Informatics, Aristotle University of Thessaloniki (2007)
Bengio, Y.: Learning deep architectures for ai. Foundations and Trends in Machine Learning 2(2), 1–127 (2009)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)
McClelland, J.L., Rumelhart, D.E., the PDP Research Group (eds.): A learning algorithm for boltzmann machines. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 282–317 (1986)
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.-T.: Nus-wide: A real-world web image database from national university of singapore. In: Proc. of ACM Conf. on Image and Video Retrieval (CIVR 2009), Santorini, Greece (2009)
Li, H., Wang, M., Hua, X.-S.: Msra-mm 2.0: A large-scale web multimedia dataset. In: International Conference on Data Mining Workshops, vol. 0, pp. 164–169 (2009)
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Liu, Y., Shao, J. (2010). High Dimensionality Reduction Using CUR Matrix Decomposition and Auto-encoder for Web Image Classification. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_1
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DOI: https://doi.org/10.1007/978-3-642-15696-0_1
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