1Department of Knowledge‐Based Information Engineering, Toyohashi University of Technology, Tempaku, Toyohashi 440, Japan
Tóm tắt
AbstractThe Non‐linear Iterative Partial Least Squares (NIPALS) algorithm is used in principal component analysis to decompose a data matrix into score vectors and eigenvectors (loading vectors) plus a residual matrix. NIPALS starts with some guessed starting vector. The principal components obtained by NIPALS depends on the starting vector; the first principal component could not always be computed. Wold has suggested a starting vector for NIPALS, but we have found that even if this starting vector is used, the first principal component cannot be obtained in all cases. The reason why such a situation occurs is explained by the power method. A simple modification of the original NIPALS procedure to avoid getting smaller eigenvalues is presented.
Từ khóa
Tài liệu tham khảo
Wold H., 1966, Research Papers in Statistics, 411
Wold S., 1983, Food Research and Data Analysis, 147
