| COMMENTS ON THE NIPALS ALGORITHM |
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| 引用本文： | YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI Department of Knowledge-Based Information Engineering,Toyohashi University of Technology,Tempaku.Toyohashi,Japan. COMMENTS ON THE NIPALS ALGORITHM[J]. 地理学报(英文版), 1990, 0(1) |
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| 作者姓名： | YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI Department of Knowledge-Based Information Engineering Toyohashi University of Technology Tempaku.Toyohashi Japan |
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| 作者单位： | YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI Department of Knowledge-Based Information Engineering，Toyohashi University of Technology，Tempaku.Toyohashi 440，Japan |
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| 摘 要： | The Non-linear lterative Partial Least Squares(NIPALS)algorithm is used in principal componentanalysis to decompose a data matrix into score vectors and eigenvectors(loading vectors)plus a residualmatrix.N1PALS starts with some guessed starting vector.The principal components obtained by NIPALSdepends on the starting vector;the first principal component could not always be computed.Wold hassuggested a starting vector for NIPALS,but we have found that even if this starting vector is used,thefirst principal component cannot be obtained in all cases.The reason why such a situation occurs isexplained by the power method.A simple modification of the original NIPALS procedure to avoid gettingsmaller eigenvalues is presented.
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COMMENTS ON THE NIPALS ALGORITHM |
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| YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI. COMMENTS ON THE NIPALS ALGORITHM[J]. Journal of Geographical Sciences, 1990, 0(1) |
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| Authors: | YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI |
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| Affiliation: | YOSHIKATSU MIYASHITA TOSHIAKI ITOZAWA HIROYUKI KATSUMI SHIN-ICHI SASAKI Department of Knowledge-Based Information Engineering,Toyohashi University of Technology,Tempaku.Toyohashi,Japan |
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| Abstract: | The Non-linear lterative 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.N1PALS 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. |
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| Keywords: | Matrix decomposition NIPALS Principal component SIMCA PLS |
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