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PCA (Principal Component Analysis)

ML Models & Algorithms
A dimensionality reduction technique that reduces high-dimensional data (like TF-IDF vectors) to two dimensions (2D) for visualization while preserving semantic structure.

PCA is a powerful dimensionality reduction technique used in machine learning. Its key function is to take high-dimensional feature vectors, specifically TF-IDF vectors in this context, and reduce them to two dimensions (2D) for visualization. PCA achieves this by identifying the directions of maximum variance (principal components) and projecting the data onto them, thereby ensuring that the reduced 2D representation still reflects the original semantic structure and relationships between entities.

Related Concepts

Dimensionality Reduction Feature Extraction LDA (Latent Dirichlet Allocation) K-Means Clustering LSA (Latent Semantic Analysis)

Sources & References

Semantic AI-powered/ML-enabled Keyword Research Course
academy.mlforseo.com

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