What are some common challenges in high-dimensional data that dimensionality reduction aims to address?
- All of the above
- Computational efficiency
- Curse of dimensionality
- Overfitting
Dimensionality reduction aims to address several challenges in high-dimensional data, including the curse of dimensionality (where distance measures lose meaning), overfitting (where models fit noise), and computational efficiency (since fewer dimensions require less computing resources).
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