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Sparse model construction using coordinate descent optimization

Hong, X., Guo, Y., Chen, S. and Gao, J. (2013) Sparse model construction using coordinate descent optimization. In: Proceedings: 18th International Conference on Digital Signal Processing (DSP2013), 1 - 3 July 2013, Santorini - Greece.

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Abstract/Summary

We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:34106
Uncontrolled Keywords:Terms—lasso, linear-in-the-parameters model, regularization, leave one out errors, cross validation.

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