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Neurocomputing, 2006
We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear mixtures when some a priori information of the sources are available in the form of rough templates (references). The constrained independent component analysis (cICA) is extended to incorporate the reference signals that
Jagath C Rajapakse
exaly +3 more sources
We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear mixtures when some a priori information of the sources are available in the form of rough templates (references). The constrained independent component analysis (cICA) is extended to incorporate the reference signals that
Jagath C Rajapakse
exaly +3 more sources
Third International Workshop on Advanced Computational Intelligence, 2010
Recently, a new method that slow feature analysis (SFA), which can extract slowly varying feature of temporally varying signals, has been explored. SFA method is an extension of independent component analysis (ICA), which has been used to separate blind source signals.
Jianbin Gao, Mao Ye 0001
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Recently, a new method that slow feature analysis (SFA), which can extract slowly varying feature of temporally varying signals, has been explored. SFA method is an extension of independent component analysis (ICA), which has been used to separate blind source signals.
Jianbin Gao, Mao Ye 0001
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Eliminating indeterminacy in ICA
Neurocomputing, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Lu 0028, Jagath C. Rajapakse
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The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
The Complex Independent Component Analysis (CICA) which extends Independent Component Analysis (ICA) to complex signals has found applications in various fields. The ICA with Reference (ICA-R) has recently gained popularity in semi-blind separation of signals when a priori information of the desired sources are available in the form of reference ...
Jagath C. Rajapakse, Wenda Chen
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The Complex Independent Component Analysis (CICA) which extends Independent Component Analysis (ICA) to complex signals has found applications in various fields. The ICA with Reference (ICA-R) has recently gained popularity in semi-blind separation of signals when a priori information of the desired sources are available in the form of reference ...
Jagath C. Rajapakse, Wenda Chen
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Practical consideration for ICA
2012 International Conference on Multimedia Computing and Systems, 2012Independent component analysis (ICA) and blind source separation (BSS) refer to the problem of recovering statistically independent signals from a linear mixture. And its success for a given data set may depend crucially on performing application-dependent preprocessing steps.
Otman Chakkor +3 more
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2003
As an alternative to the conventional Hebb-type unsupervised learning, differential learning was studied in the domain of Hebb's rule [1] and decorrelation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as differential ICA.
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As an alternative to the conventional Hebb-type unsupervised learning, differential learning was studied in the domain of Hebb's rule [1] and decorrelation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as differential ICA.
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