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.
|Number of pages||6|
|Journal||Journal of Process Control|
|Early online date||14 Feb 2000|
|Publication status||Published - Apr 2000|
|Event||5th IFAC Symposium on the Dynamics and Control of Process Systems (DYCOPS-5) - Corfu, Greece|
Duration: 8 Jun 1998 → 10 Jun 1998
Bibliographical noteThis work is supported by National Science Foundation, Air Products, ALCOA, DuPont, and Fisher-Rosemount.
- Missing values
- Principal component analysis
- Principal component subspace
- Residual subspace
- Sensor reconstruction