Kernel adaptive filtering

Kernel adaptive filtering - Kerneladaptivefiltering Lecture...

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    Kernel adaptive filtering Lecture slides for EEL6502 Spring 2011 Sohan Seth
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    The big picture Adaptive filters are linear. How do we learn (continuous) nonlinear structures?
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    A particular approach Assume a parametric model … e.g. neural network Universality: The parametric model should be able  to approximate any continuous function. Universal approximation for sufficiently large  Nonlinearly  map signal to  higher  dimensional  space and . .. apply a linear filter. nonlinear
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    It’s difficulty Nonlinear performance surface Can we learn nonlinear  structure using knowledge of  linear adaptive filtering? Fix the nonlinear mapping,                      and use linear  filtering. How do we choose the mappings? Need to guarantee universal approximation! e.g. A different approach Filter order is  
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    A ‘trick’y solution
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Kernel adaptive filtering - Kerneladaptivefiltering Lecture...

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