Summary of Data Reduction & Pattern Recognition
You have studied three widely used chemometric receptor models in data reduction and pattern recognition such;
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Principal Component Analysis (PCA). PCA is a method that functions to reduce the data set dimensionality by performing a covariance analysis between the factors.
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Principal Component Analysis coupled with Absolute Principal Component Score (PCA/APCS). APCS modeling helps to reduce the uncertainty or errors in the results of pattern recognition.
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Positive Matrix Factorization (PMF). PMF produces non-negative distributions (factors) in pattern recognition.
These three models are potentially applied in the source apportionment of air pollutants. These models are user’s friendly and will help you to know the pollution hotspot in your country.
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