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(Choose 1 answer)
What is the main difference between kernel PCA and linear PCA?
A. The objective of linear PCA is to decrease the dimensionality of the space whereas the o PCA is to increase the dimensionality of the space.
B. Kernel PCA tend to uncover non-linearity structure within the dataset by increasing the di space thanks to the kernel trick.
C. Kernel PCA and Linear PCA are both Linear dimensionality reduction algorithm but they optimization method.
D. Kernel PCA tend to preserve the geometric distances between the points while reducing of the space.
