Principal Component Analysis |
Contents
CHAPTER | 1 |
Mathematical and Statistical Properties of Population Principal | 8 |
CHAPTER | 9 |
Copyright | |
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a₁ approximation assumption biplot Chapter cluster analysis coefficients columns Component number Contrasts correlation matrix correspondence analysis covariance matrix covariance or correlation criterion cut-off data set defined deleted derived described dimensionality discriminant analysis discussed in Section eigenvalues eigenvectors equal equation equivalent estimates Euclidean distance example factor analysis Figure four PCs give given interpretation ith observation j)th element Jolliffe Krzanowski kth PC last few PCs linear functions Mahalanobis distance maximized measurements methods minimized multicollinearities multivariate normal distribution multivariate normality original variables orthogonal outliers PC regression PCs account plot population possible predictor variables principal co-ordinate analysis Principal Component Analysis procedure Property A5 q PCs relationships respectively retained rotation sample covariance sample PCs scree graph second PC similar singular value decomposition statistical structure Table techniques three PCs tion total variation two-dimensional uncorrelated values variable selection variances vector x₁ zero