Orthogonal Least Squares Regression: An Efficient Approach for Parsimonious Modelling from Large Data
- When?
- Wednesday 27 July 2011, 14:00 to 15:00
- Where?
- 39BB02
- Open to:
- Staff, Students
- Speaker:
- Professor Sheng Chen, University of Southampton
The orthogonal least squares (OLS) algorithm, developed in the late 1980s for nonlinear system modelling, remains highly popular for nonlinear data modelling practicians, for the reason that the algorithm is simple and efficient, and is capable of producing parsimonious nonlinear models with good generalisation performance. Since its derivation, many enhanced variants of the OLS forward regression have been developed by incorporating the recent developments from machine learning. Notably, regularisation techniques, optimal experimental design methods and leave-one-out cross validation have been combined with the OLS algorithm. The resultant class of OLS algorithms offers the state-of-the-arts for parsimonious modelling from large data.
Other topics discussed in this talk include effective grey-box modelling by incorporating the prior knowledge naturally to the model structure, and further efficiency enhancement for the OLS forward regression modelling by implementing the branch and bound strategy. Some very recent extensions of this unified data modelling approach will also be briefly presented.
Professor Sheng Chen's biography can be found here.