A process that reduces data, such as high-dimensional vectors, for visualization while preserving semantic structure (e.g., using PCA).
Dimensionality Reduction is a machine learning technique used to simplify complex data, typically by reducing high-dimensional data (like feature vectors) into a lower dimension for visualization, while aiming to retain the original structure and variance. The specific method mentioned is Principal Component Analysis (PCA), which reduces high-dimensional TF-IDF vectors (representing entities) down to two dimensions (2D) for easier visualization in scatter plots. This process makes complex semantic relationships between entities more comprehensible for human analysis.
Sources & References
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