Answer / Shaista Khanam

Principal Component Analysis (PCA) is a linear dimension reduction technique that identifies the most important variables or features in a dataset by projecting data onto a lower-dimensional space while preserving as much variance as possible. Kernel Principal Component Analysis (KPCA) is an extension of PCA to nonlinear data by using kernel functions to transform the data into a higher-dimensional feature space. Independent Component Analysis (ICA) is used for separating independent sources from their mixtures in multivariate signals or data.

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