TY  - GEN
ID  - cogprints3441
UR  - http://cogprints.org/3441/
A1  - Särelä, Mr Jaakko
A1  - Valpola, Dr Harri
TI  - Denoising source separation
Y1  - 2004/02//
N2  - A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for easy development of new source separation algorithms which are optimised for specific problems. In this framework, source separation algorithms are constucted around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are
considered. Some existing independent component analysis algorithms are reinterpreted within DSS framework and new, robust blind source separation algorithms are suggested.  Although DSS algorithms need not be explicitly based on objective functions, there is often an implicit objective function that is optimised. The exact relation between the denoising procedure and the objective function is derived and a useful approximation of the objective function is presented.  In the experimental section, various DSS schemes  are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and overcomplete,  nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing.

AV  - public
KW  - blind source separation
KW  -  BSS
KW  -  prior information
KW  -  denoising
KW  - 
denoising source separation
KW  -  DSS
KW  -  independent component analysis
KW  -  ICA
KW  -  magnetoencephalograms
KW  -  MEG
KW  -  CDMA
ER  -

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