Fuzzy principal component analysis and its applications in QSAR studies

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Fuzzy principal component analysis and its applications in QSAR studies (EN)

Sarbu, C. (EN)

Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Χημείας (EL)
Sarbu, C. (EN)

Principal component analysis (PCA) is a favorite tool in analytical chemistry and other technical fields for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard, one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper it is discussed and applyed a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs): the first principal component explains 87.25% of the total variance as compared to only 59.95 for PCA. Even more, PCA showed only a partial separation of scores (PAHs) onto the plane described by the first two principal components, whereas a much sharper differentiation of the PAHs, from carcinogenic point of view, is observed when FPCA is applied. (EN)

pca (EN)

Revista De Chimie (EN)



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