How to write kernelized version of Pearson Correlation without any inbuilt function?
I want to compute kernelized version of Pearson correlation, so I have to write the code instead of using the corrcoef command, according to formula I have to write the cov and std separately, I used dot product trick for cov and when I am using it for std the result is not true (this trick)
Actually I font know exactly how to input kernel (for example Gaussian in std )
for i=1:p;
one_vector(1:size(Signalsi,1,1)) = 1;
mu = (one_vector * Signalsi,1) / size(Signalsi,1,1);
A_mean_subtract = Signalsi,1 - mu(one_vector, :);
for j=1:116
u=(A_mean_subtract(:,j));
for z=1:116;
v=(A_mean_subtract(:,z));
r=sqrt(sum((u-v).^2)); ---- cov in Gaussian form ( instead of u'*v) I put u and v in gaussian kernel formula
gamma=1/(2*100^2);
k=exp(-gamma*(r^2));
covB(j,z) = k / (size(Signalsi,1,1) - 1);---- it is correct
K_R(j,z)= covB(j,z)/sqrt((u'*u)*(v'*v);---- I know that I have to kernelized this part like above but before that the result is not correct in sipmle form (not kernelized) let alone kernel form
end
end
kernel correlation
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
add a comment |
I want to compute kernelized version of Pearson correlation, so I have to write the code instead of using the corrcoef command, according to formula I have to write the cov and std separately, I used dot product trick for cov and when I am using it for std the result is not true (this trick)
Actually I font know exactly how to input kernel (for example Gaussian in std )
for i=1:p;
one_vector(1:size(Signalsi,1,1)) = 1;
mu = (one_vector * Signalsi,1) / size(Signalsi,1,1);
A_mean_subtract = Signalsi,1 - mu(one_vector, :);
for j=1:116
u=(A_mean_subtract(:,j));
for z=1:116;
v=(A_mean_subtract(:,z));
r=sqrt(sum((u-v).^2)); ---- cov in Gaussian form ( instead of u'*v) I put u and v in gaussian kernel formula
gamma=1/(2*100^2);
k=exp(-gamma*(r^2));
covB(j,z) = k / (size(Signalsi,1,1) - 1);---- it is correct
K_R(j,z)= covB(j,z)/sqrt((u'*u)*(v'*v);---- I know that I have to kernelized this part like above but before that the result is not correct in sipmle form (not kernelized) let alone kernel form
end
end
kernel correlation
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
add a comment |
I want to compute kernelized version of Pearson correlation, so I have to write the code instead of using the corrcoef command, according to formula I have to write the cov and std separately, I used dot product trick for cov and when I am using it for std the result is not true (this trick)
Actually I font know exactly how to input kernel (for example Gaussian in std )
for i=1:p;
one_vector(1:size(Signalsi,1,1)) = 1;
mu = (one_vector * Signalsi,1) / size(Signalsi,1,1);
A_mean_subtract = Signalsi,1 - mu(one_vector, :);
for j=1:116
u=(A_mean_subtract(:,j));
for z=1:116;
v=(A_mean_subtract(:,z));
r=sqrt(sum((u-v).^2)); ---- cov in Gaussian form ( instead of u'*v) I put u and v in gaussian kernel formula
gamma=1/(2*100^2);
k=exp(-gamma*(r^2));
covB(j,z) = k / (size(Signalsi,1,1) - 1);---- it is correct
K_R(j,z)= covB(j,z)/sqrt((u'*u)*(v'*v);---- I know that I have to kernelized this part like above but before that the result is not correct in sipmle form (not kernelized) let alone kernel form
end
end
kernel correlation
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
I want to compute kernelized version of Pearson correlation, so I have to write the code instead of using the corrcoef command, according to formula I have to write the cov and std separately, I used dot product trick for cov and when I am using it for std the result is not true (this trick)
Actually I font know exactly how to input kernel (for example Gaussian in std )
for i=1:p;
one_vector(1:size(Signalsi,1,1)) = 1;
mu = (one_vector * Signalsi,1) / size(Signalsi,1,1);
A_mean_subtract = Signalsi,1 - mu(one_vector, :);
for j=1:116
u=(A_mean_subtract(:,j));
for z=1:116;
v=(A_mean_subtract(:,z));
r=sqrt(sum((u-v).^2)); ---- cov in Gaussian form ( instead of u'*v) I put u and v in gaussian kernel formula
gamma=1/(2*100^2);
k=exp(-gamma*(r^2));
covB(j,z) = k / (size(Signalsi,1,1) - 1);---- it is correct
K_R(j,z)= covB(j,z)/sqrt((u'*u)*(v'*v);---- I know that I have to kernelized this part like above but before that the result is not correct in sipmle form (not kernelized) let alone kernel form
end
end
kernel correlation
kernel correlation
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
edited Nov 15 '18 at 9:11
Sagar Zala
2,38441337
2,38441337
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
asked Nov 15 '18 at 8:27
Hessam AhmadiHessam Ahmadi
11
11
add a comment |
add a comment |
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