Dimensionality reduction techniques are methods used to reduce the number of features or variables in a dataset while preserving its essential characteristics. These techniques help simplify complex data, making it easier to visualize, analyze, and process, particularly in the context of machine learning applications such as terahertz imaging data analysis.
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