Abstract
The Multivariate polynomials model provides an effective way to describe complex nonlinear input-output relationships as it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high dimensional and high order problems, multivariate polynomial regression becomes impractical due to its prohibitive number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomials model to circumvent the dimensionality problem with some compromise in the approximation capability. When applied to multi-modal biometrics fusion, this model is demonstrated to improve the combined classification performance in terms of classification accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002 |
| Pages | 788-792 |
| Number of pages | 5 |
| Publication status | Published - 2002 Dec 1 |
| Event | Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 - Singapore, Singapore Duration: 2002 Dec 2 → 2002 Dec 5 |
Publication series
| Name | Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002 |
|---|
Other
| Other | Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 02/12/2 → 02/12/5 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
}
Multi-Modal Biometrics Fusion : Beyond Optimal Weighting. / Toh, Kar Ann; Yau, Wei Yun.
Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002. 2002. p. 788-792 (Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Multi-Modal Biometrics Fusion
T2 - Beyond Optimal Weighting
AU - Toh, Kar Ann
AU - Yau, Wei Yun
PY - 2002/12/1
Y1 - 2002/12/1
N2 - The Multivariate polynomials model provides an effective way to describe complex nonlinear input-output relationships as it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high dimensional and high order problems, multivariate polynomial regression becomes impractical due to its prohibitive number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomials model to circumvent the dimensionality problem with some compromise in the approximation capability. When applied to multi-modal biometrics fusion, this model is demonstrated to improve the combined classification performance in terms of classification accuracy.
AB - The Multivariate polynomials model provides an effective way to describe complex nonlinear input-output relationships as it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high dimensional and high order problems, multivariate polynomial regression becomes impractical due to its prohibitive number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomials model to circumvent the dimensionality problem with some compromise in the approximation capability. When applied to multi-modal biometrics fusion, this model is demonstrated to improve the combined classification performance in terms of classification accuracy.
UR - http://www.scopus.com/inward/record.url?scp=2342431919&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=2342431919&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:2342431919
SN - 9810474806
SN - 9789810474805
T3 - Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002
SP - 788
EP - 792
BT - Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002
ER -