gaussian kernel smoothing, how to optimize parameter sigma?
1 view (last 30 days)
Show older comments
Hi, my question is how to find an optimal standard deviation for the gaussian kernel filter smoothing?
too large, we are losing amplitude, too small, it can be still noisy
Are there standard methods to optimize this choice? on which metrics?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/162143/image.png)
x= (0:0.1:7)';
y = sin(x);
y_=y + 0.3*randn(size(y)); %noisy signal
y__ = zeros(length(x), 3); % reconstructs
for i=1:length(x)
%test different gaussian sigmas
k = exp( -(x-repmat(x(i),length(x),1)).^2 / (2*.2^2) ) ;
y__(i,1) = k'*y_ / sum(k);
k = exp( -(x-repmat(x(i),length(x),1)).^2 / (2*.5^2) ) ;
y__(i,2) = k'*y_ / sum(k);
k = exp( -(x-repmat(x(i),length(x),1)).^2 / (2*.8^2) ) ;
y__(i,3) = k'*y_ / sum(k);
end
plot([y y_ y__])
0 Comments
Answers (1)
Junpeng Lao
on 9 Oct 2015
Hey Cyril, I come across this paper might be related to your question: http://www.princeton.edu/~samory/Papers/adaptiveKR.pdf
0 Comments
See Also
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!