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A-optimality orthogonal forward regression algorithm using branch and bound

Hong, Xia, Chen, Sheng and Harris, Chris J. (2008) A-optimality orthogonal forward regression algorithm using branch and bound IEEE Transactions on Neural Networks, 19, (11), pp. 1961-1967. (doi:10.1109/TNN.2008.2003251). (PMID:19000965).

Record type: Article


In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm

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Published date: November 2008
Organisations: Southampton Wireless Group


Local EPrints ID: 266894
PURE UUID: 338bac0b-6664-4029-a07b-a43a12380958

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Date deposited: 10 Nov 2008 11:09
Last modified: 18 Jul 2017 07:11

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Author: Xia Hong
Author: Sheng Chen
Author: Chris J. Harris

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