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Both principal curve analysis and kernel PCA provide the ability to find nonlinear PCA. Kernel PCA does this by finding principal components in a higher dimensional space. Principal curve analysis is done by finding a curve such that the distance between the points and the curve is minimized. Since the underlying intuition of two methods is different, I think they will give different results.

In what situations should one prefer one technique over another?

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