# How to related PCA output to the original data?

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Yaser Khojah on 18 Apr 2019
Edited: NN on 4 Dec 2020
Hello,
I'm new to PCA and I would like to learn the outcome of pca function. I have read the document and checked others works but I'm a bit confused on how to related the results (wcoeff, latent, explained) to the original data. For example, I'm using the example from the document as below. I understand the (wcoeff) presents the eigenvector vectors. the (latent) presents the eigenvalues. the (explained) is the percentage of the total variance explained by each principal component. NOW, how are all these information are related to the main data which is the ingredients here? how do I know from looking at the results in (explained) that the 55 % is related to which variables or columns in the ingredients matrix?
[wcoeff,~,latent,~,exp
lained] = pca(ingredients,'VariableWeights','variance')
wcoeff = 4×4
-2.7998 2.9940 -3.9736 1.4180
-8.7743 -6.4411 4.8927 9.9863
2.5240 -3.8749 -4.0845 1.7196
9.1714 7.5529 3.2710 11.3273
latent = 4×1
2.2357
1.5761
0.1866
0.0016
explained = 4×1
55.8926
39.4017
4.6652
0.0406
NN on 4 Dec 2020
i have gone through the discussion to understand PCA .I am doing a forecasting problem with neural network and used the below syntax for finding out PCA components for reducing the dimension of training and testing data .
coeff = pca(X)
Can i use the output of this command (coeff matrix) as new training and testing data for neural network ad use it for forecasting?
I have 9 input features for forecasting.How can i plot the contribution rates of each feature and prinicpal components against the variance to know the contribution of features and dimension reduction?
kindly help