Principal component analysis (PCA), based on non-linear iterative partial least squares (NIPALS) coupled with a cross-validation approach, is applied to data obtained from the chemical analysis of rainwater. The correlation between variables is obtained and their sources identified. The classification of samples into groups by PCA is also investigated. The problem of data scaling and the evaluation of methods for assessing the number of significant components in the data are also discussed.
机构:
Changwon Natl Univ, Dept Stat, Chang Won 51140, South Korea
Seoul Natl Univ, Dept Stat, Seoul 08826, South KoreaChangwon Natl Univ, Dept Stat, Chang Won 51140, South Korea
Kim, Kipoong
Park, Jaesung
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Seoul Natl Univ, Dept Stat, Seoul 08826, South KoreaChangwon Natl Univ, Dept Stat, Chang Won 51140, South Korea
Park, Jaesung
Jung, Sungkyu
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Seoul Natl Univ, Dept Stat, Seoul 08826, South Korea
Seoul Natl Univ, Inst Data Innovat Sci, 1 Gwanak Ro, Seoul 08826, South KoreaChangwon Natl Univ, Dept Stat, Chang Won 51140, South Korea