PCA and t-SNE Explained: Two Ways to Visualize High-Dimensional Word Embeddings

Word embeddings represent words as dense vectors in spaces of 100 to 300 dimensions, making them impossible to visualize directly. Dimensionality reduction techniques help compress these high-dimensional vectors into two or three dimensions while preserving meaningful relationships. Principal Component Analysis (PCA) is a linear method that captures broad structural trends and is useful for spotting outliers and global patterns. t-SNE, by contrast, prioritizes keeping nearby points close together in the reduced space, making it better suited for revealing tight word clusters and local groupings. Practitioners often use both methods together — PCA for a global overview and t-SNE for uncovering finer cluster structures within embeddings.