## What is dimensionality reduction?

Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.

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ICA is a specialized dimensionality reduction technique that is used for finding independent components within a multivariate signal.

Principal Component Analysis (PCA) is a dimension reduction technique.

The curse of dimensionality refers to the potential dangers associated with modeling from a dataset that has a large number of features.