Dimensionality reduction techniques are methods used to reduce the number of input variables in a dataset, simplifying the data while retaining its essential features. These techniques are crucial in handling biosensor data analysis as they help in visualizing high-dimensional data, improving computational efficiency, and enhancing the performance of machine learning algorithms by eliminating noise and irrelevant features.
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