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"Denoising source separation"^^ .
"A new algorithmic framework called denoising source separation (DSS)\n is introduced. The main benefit of this framework is that it allows\n for easy development of new source separation algorithms which are\n optimised for specific problems. In this framework, source\n separation algorithms are constucted around denoising\n procedures. The resulting algorithms can range from almost blind to\n highly specialised source separation algorithms. Both simple linear\n and more complex nonlinear or adaptive denoising schemes are\n considered. Some existing independent component analysis algorithms\n are reinterpreted within DSS framework and new, robust blind source\n separation algorithms are suggested. Although DSS algorithms need\n not be explicitly based on objective functions, there is often an\n implicit objective function that is optimised. The exact relation\n between the denoising procedure and the objective function is\n derived and a useful approximation of the objective function is\n presented. In the experimental section, various DSS schemes are\n applied extensively to artificial data, to real\n magnetoencephalograms and to simulated CDMA mobile network signals.\n Finally, various extensions to the proposed DSS algorithms are\n considered. These include nonlinear observation mappings,\n hierarchical models and overcomplete, nonorthogonal feature spaces.\n With these extensions, DSS appears to have relevance to many\n existing models of neural information processing."^^ .
"2004-02" .
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"Harri"^^ .
"Valpola"^^ .
"Harri Valpola"^^ .
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"Jaakko"^^ .
"Särelä"^^ .
"Jaakko Särelä"^^ .
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"Denoising source separation (PDF)"^^ .
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"JS_DSS.pdf"^^ .
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"Denoising source separation (Image (PNG))"^^ .
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"preview.png"^^ .
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"Denoising source separation (Indexer Terms)"^^ .
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"indexcodes.txt"^^ .
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"HTML Summary of #3493 \n\nDenoising source separation\n\n" .
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"Statistical Models" .
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"Machine Learning" .
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"Neural Nets" .
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"Artificial Intelligence" .
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