Visualizing High-Dimensional Embeddings with PCA and t-SNE

When working with high-dimensional embeddings—such as 256-dimensional vectors that lie on a hypersphere after training—it's often useful to project them into 2D or 3D space to inspect cluster structure or class separation. Two widely used techniques for this purpose are Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Em ...

Posted on Sat, 20 Jun 2026 17:32:46 +0000 by kusal