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Generalization of Quantification for PLS Correlation
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 Title & Authors
Generalization of Quantification for PLS Correlation
Yi, Seong-Keun; Huh, Myung-Hoe;
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This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $a^ta+b^tb+c^tc
Partial Least Squares(PLS);generalization of quantification for PLS correlation;
 Cited by
Han, S.-T. (1995). Quantification Approach to Ranked Data Analysis, Doctoral Dissertation, Korea University.

Helland, I. (2005). Partial least squares regression, The Encyclopedia of Statistical Sciences, Second Edition (edited by Kotz), 5957-5962.

Huh, M.-H. (1999). Quantification Methods for Multivariate Data, Free Academy, Seoul.

Huh, M.-H., Lee, Y. and Yi, S. (2007). Visualizing (X; Y ) data by partial least squares method, Korean Journal of Applied Statistics, 20, 345-355. crossref(new window)

Husson, F. and Pages, J. (2005). Scatter plot and additional variables, Journal of Applied Statistics, 32, 341-349. crossref(new window)

Park, M. and Huh, M.-H. (1996). Quantification plots for several sets of variables, The Journal of Korean Statistical Society, 25, 589-601.

Westerhuis, J. A., Kourti, T. and Macgregor, J. F. (1998). Analysis of Multiblock and Hierarchical PCA and PLS Models, Journal of Chemometrics, 12, 301-321. crossref(new window)

Wold, S., Esbensen, K. and Geladi, P. (1987). Principal component analysis, Chemometrics and Intelligent Laboratory Systems, 2, 37-52. crossref(new window)

Yi, S. K. (2007). Quantification Method for Partial Least Squares and its Generalization, Doctoral Dissertation, Korea University.