Dimension reduction refers to the process of reducing the number of features or variables in a dataset while preserving the important information. It helps simplify complex datasets by eliminating less significant or redundant attributes. This reduction can make data analysis and visualization easier and more efficient while still capturing the main patterns and characteristics of the original data.
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