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» A spatially robust ICA algorithm for multiple fMRI data sets
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ISBI
2002
IEEE
14 years 6 months ago
A spatially robust ICA algorithm for multiple fMRI data sets
In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all ...
Ana S. Lukic, Miles N. Wernick, Lars Kai Hansen, J...
ICIP
2002
IEEE
14 years 6 months ago
An ICA algorithm for analyzing multiple data sets
In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to t...
Ana S. Lukic, Lars Kai Hansen, Miles N. Wernick, S...
ICA
2004
Springer
13 years 10 months ago
Second-Order Blind Source Separation Based on Multi-dimensional Autocovariances
SOBI is a blind source separation algorithm based on time decorrelation. It uses multiple time autocovariance matrices, and performs joint diagonalization thus being more robust th...
Fabian J. Theis, Anke Meyer-Bäse, Elmar Wolfg...
ISCAS
2008
IEEE
145views Hardware» more  ISCAS 2008»
13 years 11 months ago
Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia
— Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is...
Vamsi K. Potluru, Vince D. Calhoun
ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
13 years 11 months ago
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way...
Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Rob...