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ICASSP
2011
IEEE
14 years 9 months ago
A sliding-window online fast variational sparse Bayesian learning algorithm
In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. A sequential decision rule for i...
Thomas Buchgraber, Dmitriy Shutin, H. Vincent Poor
ICASSP
2011
IEEE
14 years 9 months ago
Compressed learning of high-dimensional sparse functions
This paper presents a simple randomised algorithm for recovering high-dimensional sparse functions, i.e. functions f : [0, 1]d → R which depend effectively only on k out of d va...
Karin Schnass, Jan Vybíral
ICASSP
2008
IEEE
16 years 14 days ago
Frequency domain selective tap adaptive algorithms for sparse system identification
We propose a new low complexity and fast converging frequencydomain adaptive algorithm for sparse system identification. This is achieved by exploiting the MMax and SP tap-select...
Andy W. H. Khong, Xiang Lin, Milos Doroslovacki, P...
ICA
2007
Springer
15 years 10 months ago
Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1, . . . , T, for the problem of underdetermined Sparse Component Analysis (SCA)....
Nima Noorshams, Massoud Babaie-Zadeh, Christian Ju...
ICONIP
2008
15 years 7 months ago
Improved Sparse Bump Modeling for Electrophysiological Data
Bump modeling is a method used to extract oscillatory bursts in electrophysiological signals, who are most likely to be representative of local synchronies. In this paper we presen...
François B. Vialatte, Justin Dauwels, Jordi...