Dimensionality Reduction
Techniques to reduce the number of input features while preserving important information (PCA, t-SNE, autoencoders).
Related Concepts
- PCA: Explore how PCA relates to Dimensionality Reduction
- t-SNE: Explore how t-SNE relates to Dimensionality Reduction
- Feature Selection: Explore how Feature Selection relates to Dimensionality Reduction
- Curse of Dimensionality: Explore how Curse of Dimensionality relates to Dimensionality Reduction
Why It Matters
Understanding Dimensionality Reduction is crucial for anyone working with machine learning fundamentals. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Curse of Dimensionality
Challenges arising when working with high-dimensional data, including data sparsity and computational complexity.
Curse of Dimensionality
Phenomena where algorithms become inefficient as dimensionality increases, including data sparsity and distance concentration.
Feature Selection
Choosing the most relevant features from available data to reduce dimensionality and improve model performance.