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Backward elimination methods for associative memory network pruning

Backward elimination methods for associative memory network pruning
Backward elimination methods for associative memory network pruning
Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based pruning formula contain some orthogonalisation between the pruned model and the deleted regressor. The basis pursuit cost function is derived as a simple formula without need for an orthogonalisation process. These different approaches to parsimonious data based modelling are applied to the same numerical examples in parallel to demonstrate their computational effectiveness.
90-99
Hong, X.
0a733642-067b-46e5-84db-f610140c22cb
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M.
28735aef-658a-4120-853f-29e986c94fc4
Chen, S.
721af489-6f76-4a22-a932-50a37f2bdd67
Hong, X.
0a733642-067b-46e5-84db-f610140c22cb
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M.
28735aef-658a-4120-853f-29e986c94fc4
Chen, S.
721af489-6f76-4a22-a932-50a37f2bdd67

Hong, X., Harris, C.J., Brown, M. and Chen, S. (2004) Backward elimination methods for associative memory network pruning. International Journal of Hybrid Intelligent Systems, 1 (2), 90-99. (doi:10.3233/HIS-2004-11-211).

Record type: Article

Abstract

Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based pruning formula contain some orthogonalisation between the pruned model and the deleted regressor. The basis pursuit cost function is derived as a simple formula without need for an orthogonalisation process. These different approaches to parsimonious data based modelling are applied to the same numerical examples in parallel to demonstrate their computational effectiveness.

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More information

e-pub ahead of print date: February 2004
Published date: 13 September 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 258873
URI: https://eprints.soton.ac.uk/id/eprint/258873
PURE UUID: d1e97657-a13c-4611-a863-19c5bbb81b20

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Date deposited: 23 Feb 2004
Last modified: 07 Mar 2019 17:30

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