Determining the number of principal components for best reconstruction

S. Joe QIN*, Ricardo DUNIA

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

198 Citations (Scopus)

Abstract

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 are 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. © 2000 IFAC. Published by Elsevier Science Ltd.
Original languageEnglish
Pages (from-to)245-250
Number of pages6
JournalJournal of Process Control
Volume10
Issue number2-3
Early online date14 Feb 2000
DOIs
Publication statusPublished - Apr 2000
Externally publishedYes
Event5th IFAC Symposium on the Dynamics and Control of Process Systems (DYCOPS-5) - Corfu, Greece
Duration: 8 Jun 199810 Jun 1998

Bibliographical note

This work is supported by National Science Foundation, Air Products, ALCOA, DuPont, and Fisher-Rosemount.

Keywords

  • Missing values
  • Principal component analysis
  • Principal component subspace
  • Residual subspace
  • Sensor reconstruction

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