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I am working on sparse recovery for a classification task. I use Pine hyperspectral dataset which is a freely available dataset and this image contains 200 Dimension (Depth/channels/bands). In addition, this dataset contains labels (Ground truth information). Thus I extract the corresponding pixels for each class and now I want to visualize them to see really how they look like. But I do know now how to visualize them for all 200 channels. I did this visualization for 3D but I want to know how can I expand this for 3D.

Here is my code for 3D, I would appreciate any help for visualizing my data in 3D Not: I simulate the dataset.

clear all
% Number of classes = 5   
B = [1 1 2; 2 3 4; 2 1 3; 4 3 4; 5 5 3];
B_vec = reshape(B, 5*3, 1);
% Image with 200 depth
A = rand(5,3, 200)*100;
A_vec = reshape(A, 5*3, 200);
%Indexing the desire classes. 
indx = find(or(B==1, B==4, B==3));
class_num = B_vec(indx);
%Extracting the corresponding pixels (base on indx)from 200D image.
for k = 1: length(indx)
img_value(k,:) = A_vec(indx(k),:);
end 
G_idx_1 = (find(class_num==1));
for k = 1:length(G_idx_1)
C_1(k,:) = img_value(G_idx_1(k),:); 
end  
G_idx_3 = (find(class_num==3));
for k = 1:length(G_idx_3)
C_3(k,:) = img_value(G_idx_3(k),:); 
end  
G_idx_4 = (find(class_num==4));
for k = 1:length(G_idx_4)
C_4(k,:) = img_value(G_idx_4(k),:);
end

X1 = [C_1'];
X2 = [C_3'];
X3 = [C_4'];
plot3(X1(1,:), X1(2,:), X1(3,:),'bo'); axis on; grid on; hold on
plot3(X2(1,:), X2(2,:), X2(3,:),'go'); 
plot3(X3(1,:), X3(2,:), X3(3,:),'ro'); hold off;
title('class1, class2, class3')
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You can use t-SNE to perform dimensionality reduction to create a 2-D or 3-D embedding space for data visualization purposes.

For Matlab you can check the implementation and the example.

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  • $\begingroup$ Thanks. It seems to be what I want. I will check it out. $\endgroup$ – morteza Nov 11 '18 at 14:41

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