DocumentCode
1899313
Title
NonGaussian subspace learning in the presence of interference
Author
Desai, Mukund ; Mangoubi, Rami
Author_Institution
Draper (C.S.) Lab., Cambridge, MA, USA
fYear
2004
fDate
18-21 July 2004
Firstpage
230
Lastpage
234
Abstract
We consider the problem of subspace learning in the presence of interference and generalized Gaussian noise, two realistic scenarios for many applications. We also explore learning in the context of a non-Euclidean generalization of the Courant-Fisher minmax characterization. Implications for learned subspace properties in the presence of Laplacian noise are discussed as well.
Keywords
Gaussian noise; direction-of-arrival estimation; interference (signal); learning (artificial intelligence); minimax techniques; multidimensional signal processing; Courant-Fisher minmax characterization; Laplacian noise; direction-of-arrival estimation; generalized Gaussian noise; interference; nonEuclidean generalization; subspace learning; Density functional theory; Direction of arrival estimation; Gaussian noise; Integrated circuit modeling; Interference; Laboratories; Laplace equations; Magnetic noise; Matched filters; Noise robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2004
Print_ISBN
0-7803-8545-4
Type
conf
DOI
10.1109/SAM.2004.1502943
Filename
1502943
Link To Document