Fusing Eigenvalues

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Universite Paris-Saclay

Kuvaus

In this paper, we propose a new regularized (penalized) covariance matrix estimator which encourages grouping of the eigenvalues by penalizing large differences (gaps) between successive eigenvalues. This is referred to as fusing eigenvalues (eFusion). The proposed penalty function utilizes Tukey’s biweight function that is widely used in robust statistics. The main advantage of the proposed method is that it has very small bias for sufficiently large values of penalty parameter. Hence, the method provides accurate grouping of eigenvalues. Such benefits of the proposed method are illustrated with a numerical example, where the method is shown to perform favorably compared to a state-of-art method.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
TilaJulkaistu - 1 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia
Kesto: 12 toukokuuta 201917 toukokuuta 2019
Konferenssinumero: 44

Julkaisusarja

NimiProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (painettu)1520-6149
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
MaaIso-Britannia
KaupunkiBrighton
Ajanjakso12/05/201917/05/2019

ID: 33983141