I have 10000 samples, each of which has 100 features. To visualize this high dimensional sample, I use 2 component PCA. Here is a the result: Here, results are colored based on a target value for each sample. Then, I tried to draw a new sample, and plot it on top of the previous visualization using
from sklearn.decomposition import PCA pca = PCA(n_components=2) feature_space_2d = pca.fit_transform(np.vstack((sample, new_draw)))
When I tried to compare these two, I realized that the visualization of original 10000 samples have essentially been rotated . You can see newly drawn sample at top left (please discard other three black points).
- I understand that adding a point would change the PCA output, but it seems so drastically to me. Would some one be able to shed some light on it?
- Is there a way to apply the same PCA transform as we did on the original samples to any drawn samples?