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Reducing the computational requirement of the orthogonal least squares algorithm

Reducing the computational requirement of the orthogonal least squares algorithm
Reducing the computational requirement of the orthogonal least squares algorithm
The orthogonal least squares (OLS) algorithm is an efficient implementation of the forward regression procedure for subset model selection. The ability to find good subset parameters with only linear increase in computational complexity makes this method attractive for practical implementations. In this paper, we will examine the computation requirement of the OLS algorithm to reduce a model of K terms to a subset, model of R terms when the number of training data available is N . We will show that in the case where N >> Ii, we can reduce the computation requirement by introducing an unitary transformation on the problem.
529-532
Chng, E. S.
fea46228-47bf-4963-83d7-b7b5591183de
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Chng, E. S.
fea46228-47bf-4963-83d7-b7b5591183de
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a

Chng, E. S., Chen, S. and Mulgrew, B. (1994) Reducing the computational requirement of the orthogonal least squares algorithm. ICASSP-94, Adelaide, Australia. 18 - 21 Apr 1994. pp. 529-532 .

Record type: Conference or Workshop Item (Paper)

Abstract

The orthogonal least squares (OLS) algorithm is an efficient implementation of the forward regression procedure for subset model selection. The ability to find good subset parameters with only linear increase in computational complexity makes this method attractive for practical implementations. In this paper, we will examine the computation requirement of the OLS algorithm to reduce a model of K terms to a subset, model of R terms when the number of training data available is N . We will show that in the case where N >> Ii, we can reduce the computation requirement by introducing an unitary transformation on the problem.

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More information

Published date: April 1994
Additional Information: IEEE International Conference on Acoustics, Speech, and Singal Processing (Adelaide, Australia), April 19-22, 1994. Event Dates: April 19-22, 1994 Organisation: IEEE Signal Processing Society
Venue - Dates: ICASSP-94, Adelaide, Australia, 1994-04-18 - 1994-04-21
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251095
URI: http://eprints.soton.ac.uk/id/eprint/251095
PURE UUID: f48205ef-ff53-44c8-b847-ff6bcb4b58d5

Catalogue record

Date deposited: 12 Oct 1999
Last modified: 03 Feb 2022 17:48

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Contributors

Author: E. S. Chng
Author: S. Chen
Author: B. Mulgrew

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