Limitations, Assumptions Watch-Outs of Principal Component Analysis

Hey!, I know enough of PCA, but why not?

It is a great algorithm with certain watch-outs which can mostly be tackled with certain adjustments in the Vanilla PCA.

ENJOY!!

Kernel PCA, on the other hand, first transforms the data into an even higher-dimensional space where: C

And only then projects the data onto the eigenvectors of that matrix, just like regular PCA. The kernel trick refers to performing the computation without actually computing Φ(x). This is possible only if Φ is chosen such that it has a known corresponding kernel. KPCA doesn’t always cut it, so depending on your dataset, you may need to look at other non-linear dimensionality reduction techniques, such as LLE, isomap, or t-SNE.

PCA fails in Left figure

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