4.5 Article Proceedings Paper

Determining the number of principal components for best reconstruction

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JOURNAL OF PROCESS CONTROL
卷 10, 期 2-3, 页码 245-250

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ELSEVIER SCI LTD
DOI: 10.1016/S0959-1524(99)00043-8

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principal component analysis; missing values; sensor reconstruction; principal component subspace; residual subspace

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A well-defined variance of reconstruction error (VRE) is proposed to determine the number of principal components in a PCA model for best reconstruction. Unlike most other methods in the literature, this proposed VRE method has a guaranteed minimum over the number of PC's corresponding to the best reconstruction. Therefore, it avoids the arbitrariness of other methods with monotonic indices. The VRE can also be used to remove variables that ape little correlated with others and cannot be reliably reconstructed from the correlation-based PCA model. The effectiveness of this method is demonstrated with a simulated process. (C) 2000 IFAC. Published by Elsevier Science Ltd. All rights reserved.

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