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Rotate PCA's
Contributor I
Hi there,
i am currently working on some stuff, where i want to reduce a given number of attributes to a much smaller set. So i started with a pca and a correlation matrix as visualization. So far so good, now i would like to rotate and load the factors with a derivate bigger 1 ( in my case around 20 factors ), so i am able to reduce my given attributes. Maybe i am just blind, but till now, i haven't found a proper setup to do this, any help is highly appreciated.
Greets
i am currently working on some stuff, where i want to reduce a given number of attributes to a much smaller set. So i started with a pca and a correlation matrix as visualization. So far so good, now i would like to rotate and load the factors with a derivate bigger 1 ( in my case around 20 factors ), so i am able to reduce my given attributes. Maybe i am just blind, but till now, i haven't found a proper setup to do this, any help is highly appreciated.
Greets
0
Answers
what exactly do you mean by rotate? It seems to me, I'm not familiar with this. Where do the derivates come from?
Greetings,
Sebastian
well this is a well known method ( in german "exploratorische Faktorenanalyse" ). First you start with a datamatrix ( here the dataset ) . Upon which you get a correlation matrix - correlation of each attributes. With the correlation matrix you get a factor charged matrix. Within PCA ( in rapidminer ) i come up to this point ( a matrix, where all attributes are loaded on the selected factors ). But then you need to rotate the factor charged matrix, so you "load" a attribute as high as possible on one factor. I know that in SPSS there is a way to do this, but i don t have SPSS, so i tried rapidminer, but can t find the rotation of the factor charged matrix.
Due to the fact that i am on work, i don t have my rapidminer setup visible, but as far as i remember following setup should fit mine:
ExcelExampleSet
PCA
Correlation Matrix ( Visualization )
Greetings from Germany
p.s: derivates are given by PCA in rapidminer
[edit] Rotation should be something like the VARIMAX or QUARTIMAX algorithm
currently neither the Varimax or Quartimax algorithm is available, but I added it on the agenda. We are going to enlarge the functionality in this area with the next versions.
Greetings,
Sebastian