Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds. Export

@article{citeulike:1276510, abstract = {Principal component analysis (PCA) is a favorite tool in chemometrics 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 with 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 outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20\% of the total variance and PCA accounts only for 69.75\%.}, address = {Department of Chemistry, Computational Research on Materials Institute (CROMIUM), The University of Memphis, Memphis, Tennessee 38152-6060, USA. tomc@unt.edu}, author = {Cundari, T. R. and S\^{a}rbu, C. and Pop, H. F.}, citeulike-article-id = {1276510}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12444733}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12444733}, issn = {0095-2338}, journal = {J Chem Inf Comput Sci}, keywords = {fuzzy, pca, robust}, number = {6}, pages = {1363--1369}, posted-at = {2007-05-04 07:16:46}, priority = {2}, title = {Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12444733}, volume = {42}, year = {2002} }

TY - JOUR ID - citeulike:1276510 L3 - citeulike-article-id:1276510 N2 - Principal component analysis (PCA) is a favorite tool in chemometrics 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 with 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 outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%. IS - 6 JF - J Chem Inf Comput Sci SN - 0095-2338 AD - Department of Chemistry, Computational Research on Materials Institute (CROMIUM), The University of Memphis, Memphis, Tennessee 38152-6060, USA. tomc@unt.edu EP - 1369 TI - Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds. VL - 42 SP - 1363 KW - fuzzy KW - pca KW - robust AU - Cundari, TR AU - Sârbu, C AU - Pop, HF PY - 2002///c UR - http://view.ncbi.nlm.nih.gov/pubmed/12444733 ER -