Abstract
Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. PCA has been applied in many areas successfully, however, one of its problems is noise sensitivity due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the problem and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can be affected by noise due to equal initial membership values for all data points. The fact that RF-PCA2 is still based on sum-square-error is another reason for noise sensitivity.
In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm modifies the objective function of RF-PCA2 to allow some increase of sum-square-error and calculates initial membership values using data distribution. RF-PCA3 outperforms RF-PCA2, which is supported by experimental results.
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References
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (2002)
Rousseeuw, P.J.: Multivariate estimation with high breakdown point. Mathematical Statistics and Applications B, 283–297 (1985)
Lu, C.-D., Zhang, T.-Y., Du, X.-Z., Li, C.-P.: A robust kernel PCA algorithm. In: Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, pp. 3084–3087 (2004)
Lu, C., Zhang, T., Zhang, R., Zhang, C.: Adaptive robust kernel PCA algorithm. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. VI 621–624 (2003)
Yang, T.-N., Wang, S.-D.: Fuzzy auto-associative neural networks for principal component extraction of noisy data. IEEE Transaction on Neural Networks 11(3), 808–810 (2000)
Cundari, T.R., Sarbu, C., Pop, H.F.: Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds. Journal of Chemical Information and Computer Sciences 42(6), 1363–1369 (2002)
Heo, G., Gader, P., Frigui, H.: RKF-PCA: Robust kernel fuzzy PCA. Neural Networks 22(5-6), 642–650 (2009)
Heo, G., Kim, S.H., Woo, Y.W.: An improved robust fuzzy principal component analysis. The Journal of the Korean Institute of Maritime Information and Communication Sciences 14(5), 1093–1102 (2010)
Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22, 85–126 (2004)
Panigrahy, R.: An improved algorithm finding nearest neighbor using kd-trees. In: Proceedings of the 8th Latin American Symposium on Theoretical Informatics, pp. 387–398 (2008)
Nitin Bhatia, V.: Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security 8(2), 302–305 (2010)
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Heo, G., Kim, S.H., Woo, Y.W., Lee, I. (2011). Another Variant of Robust Fuzzy PCA with Initial Membership Estimation. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_13
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DOI: https://doi.org/10.1007/978-3-642-20042-7_13
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