Bayesian Feature Selection for Hearing Aid Personalization

A. Ypma, B. Vries, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
82 Downloads (Pure)

Abstract

We formulate hearing aid personalization as a linear regression. Since sample sizes may be low and the number of features may be high we resort to a Bayesian approach for sparse linear regression that can deal with many features, in order to find efficient representations for on-line usage. We compare to a heuristic feature selection approach that we optimized for speed. Results on synthetic data with irrelevant and redundant features indicate that Bayesian backfitting has labelling accuracy comparable to the heuristic approach (for moderate sample sizes), but takes much larger training times. We then determine features for hearing aid personalization by applying the method to hearing aid preference data.
Original languageEnglish
Title of host publicationBayesian Feature Selection for Hearing Aid Personalization, MLSP-07, Proceeings, Thessaloniki, Greece, 2007
Pages425-430
DOIs
Publication statusPublished - 2008
Eventconference; Bayesian Feature Selection for Hearing Aid Personalization, MLSP-07 -
Duration: 1 Jan 2008 → …

Conference

Conferenceconference; Bayesian Feature Selection for Hearing Aid Personalization, MLSP-07
Period1/01/08 → …
OtherBayesian Feature Selection for Hearing Aid Personalization, MLSP-07

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