scikit learn PCA dimension reduction - data lot of features and few samples

问题

I am trying to do a dimension reduction using PCA from scikit-learn. My data set has around 300 samples and 4096 features. I want to reduce the dimensions to 400 and 40. But when I call the algorithm the resulting data does have at most "number of samples" features.

from sklearn.decomposition import PCA

pca = PCA(n_components = 400)
trainData = pca.fit_transform(trainData)
testData = pca.transform(testData)

Where initial shape of trainData is 300x4096 and the resulting data shape is 300x300. Is there any way to perform this operation on this kind of data (lot of features, few samples)?

回答1:

The maximum number of principal components that can be extracted from and M x N dataset is min(M, N). Its not an algorithm issue. Fundamentally, that is the maximum number that there are.

来源：https://stackoverflow.com/questions/22557883/scikit-learn-pca-dimension-reduction-data-lot-of-features-and-few-samples