Reducing subspace, often referred to in the context of dimensionality reduction in fields such as machine learning and statistics, typically refers to a lower-dimensional representation of data that retains the essential characteristics of the original high-dimensional space. The main goal of reducing subspaces is to simplify the data while preserving relevant information, allowing for more efficient computation, enhanced visualization, or improved performance on specific tasks.
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