This question already has an answer here: Closed 11 years ago. Possible Duplicate: Comparing svd and princomp in R
I want to get singular values of a matrix in R to get the principal components, then make princomp(x) too to compare results
I am using PCA to find out which variables in my dataset are redundand due to being highly correlated with other variables. I am using princomp matlab function on the data previously normalized using
I\'m trying to create a PCA model in OpenCV to hold pixel coordinates. As an experiment I have two sets of pixel coordinates that maps out two approximate circles. Each set of coordiantes has 48 x,y p
My job is to perform gesture recognition. I want to do that by training a support vector machine using the features extracted by performing PCA(Principal component Analysis).
I\'m trying to perform PCA in EmguCV as: Matrix matrix = new Matrix(noOfSamples, noOfFeatures); // put data in matrix
I wonder if it is possible to plot pca biplot results with ggplot2. Suppose if I want to display the following biplot results with ggplot2
I\'m working with Python开发者_如何学运维 and I\'ve implemented the PCA using this tutorial. Everything works great, I got the Covariance I did a successful transform, brought it make to the original
I have been able to calculate the eigenvectors/values of my data sample (N samples of dimension M) and I would like to reduce the dimension to say 3. If i am correct i need to choose the first 3 eigen
I am now trying some stuff with PCA but it\'s very important for me to know which are the features responsible for each eigenvalue.
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