Answer Posted / Rajan Kumar Gupta

Principal Component Analysis (PCA) is a dimensionality reduction technique used to find the most important features in a dataset. Kernel Principal Component Analysis (KPCA) extends PCA to non-linear data by using a kernel trick. Independent Component Analysis (ICA) separates mixed signals into independent components, assuming that the independent sources have non-Gaussian distributions.

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