Dimension Reduction
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May 1, 2024
Updated May 9, 2025
16 minute read
An Introduction to Dimension Reduction
Dimension reduction, at its core, is the process of taking a dataset with many variables (or "dimensions") and transforming it into a dataset with fewer variables, all while trying to preserve the essential information and structure of the original data. Imagine trying to understand a complex object by looking at its shadow; the shadow is a lower-dimensional representation that can still tell you a lot about the object's shape. In data analysis and machine learning, dealing with high-dimensional data can be computationally expensive, lead to models that don't generalize well to new data (a problem known as overfitting), and make it difficult to visualize and interpret patterns. Dimension reduction techniques aim to alleviate these issues.
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