Cross-validation methods in principal component analysis: A comparison Export

@article{citeulike:2297586, abstract = {Abstract\ \ In principal component analysis (PCA), it is crucial to know how many principal components (PCs) should be retained in order to account for most of the data variability. A class of ” objective” rules for finding this quantity is the class of cross-validation (CV) methods. In this work we compare three CV techniques showing how the performance of these methods depends on the covariance matrix structure. Finally we propose a rule for the choice of the ” best” CV method and give an application to real data.}, author = {Diana, Giancarlo and Tommasi, Chiara}, citeulike-article-id = {2297586}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF02511446}, day = {23}, doi = {10.1007/BF02511446}, journal = {Statistical Methods and Applications}, month = {February}, number = {1}, pages = {71--82}, posted-at = {2008-01-28 13:39:40}, priority = {2}, title = {Cross-validation methods in principal component analysis: A comparison}, url = {http://dx.doi.org/10.1007/BF02511446}, volume = {11}, year = {2002} }

TY - JOUR ID - citeulike:2297586 L3 - citeulike-article-id:2297586 N2 - Abstract  In principal component analysis (PCA), it is crucial to know how many principal components (PCs) should be retained in order to account for most of the data variability. A class of “objective” rules for finding this quantity is the class of cross-validation (CV) methods. In this work we compare three CV techniques showing how the performance of these methods depends on the covariance matrix structure. Finally we propose a rule for the choice of the “best” CV method and give an application to real data. IS - 1 JF - Statistical Methods and Applications EP - 82 TI - Cross-validation methods in principal component analysis: A comparison VL - 11 SP - 71 AU - Diana, Giancarlo AU - Tommasi, Chiara PY - 2002/02/23/ UR - http://dx.doi.org/10.1007/BF02511446 ER -