Abstract
Principal Component Analysis (PCA) is a technique to transform the original set of variables into a smaller set of linear combinations that account for most of the original set variance. The data reduction based on the classical PCA is fruitless if outlier is present in the data. The decomposed classical covariance matrix is very sensitive to outlying observations. ROBPCA is an effective PCA method combining two advantages of both projection pursuit and robust covariance estimation. The estimation is computed with the idea of minimum covariance determinant (MCD) of covariance matrix. The limitation of MCD is when covariance determinant almost equal zero. This paper proposes PCA using the minimum vector variance (MVV) as new measure of robust PCA to enhance the result. MVV is defined as a minimization of sum of square length of the diagonal of a parallelotope to determine the location estimator and covariance matrix. The usefulness of MVV is not limited to small or low dimension data set and to non-singular or singular covariance matrix. The MVV algorithm, compared with FMCD algorithm, has a lower computational complexity; the complexity of VV is of order O(p 2).
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Gnanadesikan, R.: Method for Statistical Data Analysis of Multivariate Observations. Wiley, New York (1977)
Barnett, V., Lewis, T.: Outliers in Statistical Data, 2nd edn. Wiley, New York (1984)
Hampel, F.R., Ronchetti, E.M., Rousseuw, P.J., Stahel, W.A.: Robust Statistics. Wiley, New York (1985)
Hubert, M., Rousseeuw, P.J., vanden Branden, K.: ROBPCA: a new approach to robust principal component analysis. J. Technomet. 47, 64–79 (2005)
Djauhari, M.A.: Improved monitoring of multivariate process variability. J. Quality Technol. 37(1), 32–39 (2005)
Anderson, T.W.: An Introduction to Multivariate Statistical Analysis, 2nd edn. Wiley, New York (1984)
Alt, F.B., Smith, N.D.: Multivariate process control. Handbook Statis 7, 333–351 (1988)
Herwindiati, D.E., Djauhari, M.A., Mashuri, M.: Robust multivariate outlier labeling. J. Commun. Statis. Simul. Comput. 36(6) (2007)
Hawkins, D.M.: The feasible solution algorithm for the minimum covariance determinant estimator in multivariate data. J. Comput. Statis Data Anal. 17, 197–210 (1994)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 2nd edn. Wiley, New York (1988)
Long, F., Zhang, H., Feng, D.D.: Multimedia Information Retrieval and Management. Spinger, Berlin (2003)
Rousseeuw, P.J., van Driessen, K.: A fast algorithm for the minimum covariance determinant estimator. J. Technomet. 41, 212–223 (1999)
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Herwindiati, D.E., Isa, S.M. (2010). The New Measure of Robust Principal Component Analysis. In: Ao, SI., Gelman, L. (eds) Electronic Engineering and Computing Technology. Lecture Notes in Electrical Engineering, vol 60. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8776-8_34
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DOI: https://doi.org/10.1007/978-90-481-8776-8_34
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