Back to Galleryt-SNE
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique designed for visualization. It converts similarities between data points to joint probabilities and minimizes the Kullback-Leibler (KL) divergence between high-dimensional and low-dimensional probability distributions.
Key Parameters & Visual Influence:
• Perplexity: Balances attention between local and global aspects of the data. It roughly translates to the number of nearest neighbors considered. Low perplexity creates fragmented, tight clusters. High perplexity pulls the overall layout together.
• Learning Rate & Iterations: Control the gradient updates that reposition points on the screen.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label