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ICA
2004
Springer
10 years 16 days ago
Postnonlinear Overcomplete Blind Source Separation Using Sparse Sources
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...
Fabian J. Theis, Shun-ichi Amari
ICASSP
2008
IEEE
10 years 1 months ago
Blind source separation using monochannel overcomplete dictionaries
We propose a new approach to underdetermined Blind Source Separation (BSS) using sparse decomposition over monochannel dictionary atoms and compare it to multichannel dictionary a...
B. Vikrham Gowreesunker, Ahmed H. Tewfik
NIPS
2003
9 years 8 months ago
Sparse Representation and Its Applications in Blind Source Separation
In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K−means method. We have proved ...
Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Se...
ISNN
2005
Springer
10 years 19 days ago
Post-nonlinear Blind Source Separation Using Neural Networks with Sandwiched Structure
Abstract. This paper proposes a novel algorithm based on informax for postnonlinear blind source separation. The demixing system culminates to a neural network with sandwiched stru...
Chunhou Zheng, Deshuang Huang, Zhan-Li Sun, Li Sha...
IJON
2002
128views more  IJON 2002»
9 years 6 months ago
Extraction of a source from multichannel data using sparse decomposition
It was discovered recently that sparse decomposition by signal dictionaries results in dramatic improvement of the qualities of blind source separation. We exploit sparse decompos...
Michael Zibulevsky, Yehoshua Y. Zeevi
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