Abstract
![CDATA[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.]]
Original language | English |
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Title of host publication | 2013 18th International Conference on Digital Signal Processing, DSP 2013, Santorini, Greece, 1-3 July, 2013 |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Print) | 9781467358057 |
DOIs | |
Publication status | Published - 2013 |
Event | International Conference on Digital Signal Processing - Duration: 1 Jul 2013 → … |
Conference
Conference | International Conference on Digital Signal Processing |
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Period | 1/07/13 → … |
Keywords
- algorithms
- linear models (statistics)
- regularization