Reproducible t-SNE Results: A Step-by-Step Guide

To make t-SNE results reproducible, you can follow these steps:

1. Set a random seed: t-SNE uses random initialization, which can lead to varying outcomes with each run. Setting a random seed ensures consistent starting conditions, resulting in reproducible results.

2. Use the same data: Ensure the data remains unchanged for every algorithm run. Any data variations will affect the results.

3. Use the same hyperparameters: t-SNE has adjustable hyperparameters such as perplexity, learning rate, and number of iterations. Maintaining these parameters constant across runs guarantees reproducibility.

4. Document the process: Record every step involved in the t-SNE analysis, including the random seed used, the data, and the hyperparameters. This documentation will aid in reproducing the analysis later.

5. Test the reproducibility: Run the algorithm multiple times with the same data and hyperparameters to verify reproducibility. Consistent results across runs indicate a reproducible analysis.