dimensionality

Definition

How many components a vector or space has. It matters because of the curse of dimensionality: as dimensions grow, data becomes sparse and distances between points converge, so nearest-neighbour intuitions break down — hence the long tradition of dimensionality reduction with PCA, t-SNE, or UMAP. In model design the embedding dimension is a direct trade-off between representational capacity and compute and memory cost.