Efficient computational schemes for the orthogonal least squares algorithm

The orthogonal least squares (OLS) algorithm is an efficient implementation of the forward selection method for subset model selection. The ability to find good subset parameters with only a linearly increasing computational requirement makes this method attractive for practical implementations. We...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1995-01, Vol.43 (1), p.373-376
Main Authors: Chng, E.S., Chen, S., Mulgrew, B.
Format: Article
Language:English
Subjects:
Applied sciences
Computational complexity
Degradation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Integrated circuit modeling
Least squares methods
Linear regression
Predictive models
Signal and communications theory
Signal processing algorithms
Signal, noise
Telecommunications and information theory
Vectors
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Summary:The orthogonal least squares (OLS) algorithm is an efficient implementation of the forward selection method for subset model selection. The ability to find good subset parameters with only a linearly increasing computational requirement makes this method attractive for practical implementations. We examine the computational complexity of the algorithm and present a preprocessing method for reducing the computational requirement.< >
ISSN:1053-587X
1941-0476
DOI:10.1109/78.365331