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PCA_Principal component analysis -Dimension reduction

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NN
NN on 4 Dec 2020
Answered: Mohith Kulkarni on 7 Dec 2020
I tried to apply PCA for neural network study .For training and testing data, i used the below syntax and given the output of pca
coeff = pca(X)
Training data is 576*10 double matrix ,
Testing data is 144*10 double matrix.
When i used the above syntax, it reduced both matrices to 10*10 double.So after performing the neural network analysis, i am getting the predicted forecasted matrix also 10*10 double.How can i reverse the values matching to the input data and plot it against time.
Kindly help

Answers (1)

Mohith Kulkarni
Mohith Kulkarni on 7 Dec 2020
The pca function returns the principal component coefficients, also known as loadings, for the n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is p-by-p. Each column of coeff contains coefficients for one principal component, and the columns are in descending order of component variance.
Check the Apply PCA to new Data section in the pca documentation to learn more on how to apply the pca to training and testing data and to get predictions using the pca applied test data.

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