Abstract
Bayesian estimation is used in this paper to derive a new PCA modeling algorithm that improves the estimation accuracy by incorporating prior knowledge about the data and model. It is shown that the algorithm is more general than existing methods, PCA and MLPCA, and reduces to these techniques when a uniform prior is used. It is also shown that when no external information is available, an empirically estimated prior from the available data can still provide improved accuracy over non-Bayesian methods.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3666-3671 |
| Number of pages | 6 |
| Journal | Proceedings of the American Control Conference |
| Volume | 5 |
| DOIs | |
| State | Published - 2001 |
| Event | 2001 American Control Conference - Arlington, VA, United States Duration: Jun 25 2001 → Jun 27 2001 |
Fingerprint Dive into the research topics of 'Improving principal component analysis using Bayesian estimation'. Together they form a unique fingerprint.
Cite this
Nounou, M. N., Bakshi, B. R., Goel, P. K., & Shen, X. (2001). Improving principal component analysis using Bayesian estimation. Proceedings of the American Control Conference, 5, 3666-3671. https://doi.org/10.1109/acc.2001.946204