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Results tagged with pca
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user 132584
Principal component analysis, a technique for dimensionality reduction.
1
vote
Reconstruct low dimensional PCA projection
You can direct do that in sklearn with inverse_transform().
from sklearn.decomposition import PCA
import numpy as np
X=np.random.rand(100,32)
pca = PCA(n_components=2)
pca.fit(X)
X_reduced=pca.transform … But if you try to reduce the "inverse" X:
X_inv_red=pca.transform(X_inverse)
You get the same PCA reduction as the original X input.
You can use it in whatever 2D point.
inverse_transform() …
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