On the Exponentially Embedded Family (EEF) Rule for Model Order Selection
2012 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 19, no 9, 551-554 p.Article in journal (Refereed) Published
Model selection is an important task in many signal processing applications. In this letter, we present a generalized likelihood ratio (GLR)-based derivation of the recently proposed EEF rule in an attempt to cast EEF in the main stream of model order selection approaches and provide further insights into its theoretical foundations. We also show that EEF can be expected to behave asymptotically (in the number of data samples) similarly to the Bayesian information criterion (BIC). To evaluate the finite sample performance we consider two numerical examples, including the selection of the number of components in a Gaussian mixture model (GMM), by means of which we show that EEF behaves similarly to BIC.
Place, publisher, year, edition, pages
2012. Vol. 19, no 9, 551-554 p.
Bayesian information criterion (BIC), exponentially embedded family (EEF), Gaussian mixture model (GMM), generalized likelihood ratio (GLR), model order selection
Computer and Information Science
IdentifiersURN: urn:nbn:se:uu:diva-179551DOI: 10.1109/LSP.2012.2206583ISI: 000306520600001OAI: oai:DiVA.org:uu-179551DiVA: diva2:545463