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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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Published in: | IEEE transactions on signal processing 1995-01, Vol.43 (1), p.373-376 |
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container_end_page | 376 |
container_issue | 1 |
container_start_page | 373 |
container_title | IEEE transactions on signal processing |
container_volume | 43 |
creator | Chng, E.S. Chen, S. Mulgrew, B. |
description | 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.< > |
doi_str_mv | 10.1109/78.365331 |
format | article |
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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 |
title | Efficient computational schemes for the orthogonal least squares algorithm |
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