- Title
- An optimization-based algorithm for model selection using an approximation of Akaike's Information Criterion
- Creator
- Carvajal, Rodrigo; Urrutia, Gabriel; Agüero, Juan C.
- Relation
- 2016 Australian Control Conference (AuCC). Proceeedings of the 2016 Australian Control Conference (AuCC) (Newcastle, N.S.W. 03-04 November, 2016) p. 217-220
- Publisher Link
- http://dx.doi.org/10.1109/AUCC.2016.7868191
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- In this paper, we consider an optimization approach for model selection using Akaike's Information Criterion (AIC) by incorporating the l0-(pseudo)norm as a penalty function to the log-likelihood function. In order to reduce the numerical complexity of the optimization problem, we propose to approximate the l0-(pseudo)norm by an exponential term. We focus on problems with hidden variables - i.e. where there are random variables that we cannot measure, and the Expectation-Maximization (EM) algorithm. We illustrate the benefits of our proposal via numerical simulations.
- Subject
- signal processing algorithms; approximation algorithms; computational modeling; Australia; load modeling; cost function
- Identifier
- http://hdl.handle.net/1959.13/1343960
- Identifier
- uon:29285
- Identifier
- ISBN:9781922107909
- Language
- eng
- Reviewed
- Hits: 1207
- Visitors: 1177
- Downloads: 0
Thumbnail | File | Description | Size | Format |
---|