CODEWITHSUNDEEP
matlabploteigenvector
user1042343
user1042343

Reputation: 85

plotting multivariate data along eigenvectors

I have a data matrix contains 18 samples, each with 12 variables, D(18,12). I performed k-means clustering on the data to get 3 clusters. I want to visualize this data in 2 dimensions, specifically, along the 2 eigenvectors corresponding to the largest eigenvalues of a specific matrix, B. So, I create the plane spanned by two eigenvectors corresponding to the largest two eigenvalues:

[V,EA]=eig(B);
e1=V(:,11);
e2=V(:,12);
for i=1:12
    E(i,1)=e1(i);
    E(i,2)=e2(i);
end
Eproj=E*E';

where e1 and e2 are the eigenvectors, and E is a matrix containing those column vectors. At this point, I'm kind of stuck. I recognize that e1 and e2 are orthogonal in this 12-d space, but I have no idea how this can reduce to two dimensions so I can plot it. I believe that the projection of a data sample onto the plane would be:

Eproj*D(i,:)

for i=1...18, but I'm not sure where to go from here to plot my clusters. When I do the projection, its still in 12 dimensions.

Upvotes: 1

Views: 722

Answers (1)

ssulun
ssulun

Reputation: 46

Principal Component Analysis can help you to transform the data into 2D using the Eigenvectors.

coeff = princomp(B);
Bproj = B * coeff(:,1:2);
figure
plot(Bproj(:,1),Bproj(:,2),'*')

If you have the labels you can use the "scatter" function for a better visual. Or you can reduce the dimensionality to 3 and use "scatter3" function.

Upvotes: 2

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