How to “Compare unfold-PCA and PARAFAC”

Centering
To center the data do the following
(or use mncn in the PLS_Toolbox after matricizing the data)

Cent  = [1 0 0]; 
Scal  = [0 0 0]; 
Xmean = nprocess(X,Cent,Scal);

Before you can analyze the data with PCA, they must be rearranged into a matrix
Xmean = reshape(Xmean,10,88);

PCA 
Use truncated SVD – singular value decomposition – (or the PLS_Toolbox) to estimate the PCA model. If you don’t know what SVD is, never mind; just fit a two-component PCA model which can be done as follows.

[U,S,V] = svd(Xmean,0); 
T = U(:,1:2)*S(1:2,1:2);  % Score matrix 
P = V(:,1:2); % loading matrix

and use the plotting functions of MATLAB to see the results

plot(T(:,1),T(:,2),'.') 
for i = 1:size(T,1) 
  text(T(i,1),T(i,2),num2str(i)) 
end

PARAFAC 
Fit a two-component PARAFAC model and convert the output to scores and loadings. For plotting the PARAFAC attributes use the matrix attrib which holds the names of the attributes

 plot(B(:,1),B(:,2),'.') 
 for i = 1:size(B,1) 
   text(B(i,1),B(i,2),attrib(i,:)) 
 end

For plotting the scores from both PCA and PARAFAC do

 subplot(1,2,1) 
 plot(T(:,1),T(:,2),'.') 
 for i = 1:size(T,1) 
   text(T(i,1),T(i,2),num2str(i)) 
 end 

 subplot(1,2,2) 
 plot(A(:,1),A(:,2),'.') 
 for i = 1:size(A,1) 
   text(A(i,1),A(i,2),num2str(i)) 
 end

Note that the order and sign of the scores may differ.
This is of no concern as it is the relative positions that are important. Try e.g.

 subplot(1,2,2) 
 plot(-A(:,2),-A(:,1),'.') 
 for i = 1:size(A,1) 
   text(-A(i,2),-A(i,1),num2str(i)) 
 end

Note also that centering makes a difference.
Remember to center the data both before doing PCA and PARAFAC.