t-Distributed Stochastic Neighbor Embedding

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for dimensionality reduction and data visualization, particularly effective for high-dimensional datasets. It converts similarities between data points into joint probabilities and minimizes the Kullback-Leibler divergence between these probabilities in high and low-dimensional spaces, using a Student's t-distribution to mitigate crowding issues. It is widely used to visualize complex data like gene expression, images, or word embeddings in 2D or 3D plots.

Also known as: t-SNE, tSNE, t-distributed SNE, t-Distributed Stochastic Neighbor Embedding, t-Distributed SNE

Why learn t-Distributed Stochastic Neighbor Embedding?

Developers should learn t-SNE when working with high-dimensional data in fields like bioinformatics, natural language processing, or computer vision, as it helps uncover patterns and clusters that are not apparent in raw data. It is especially useful for exploratory data analysis, model debugging, and presenting insights to non-technical stakeholders, though it is computationally intensive and not suitable for large datasets or preserving global structure.

Learning Resources

• docs: Original t-SNE Paper by Laurens van der Maaten
• docs: Scikit-learn t-SNE Documentation
• video: StatQuest: t-SNE, Clearly Explained (YouTube)
• tutorial: Distill.pub: How to Use t-SNE Effectively
• course: Coursera: Machine Learning Specialization (includes dimensionality reduction)