t-Distributed Stochastic Neighbor Embedding

Definition:

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique primarily used for visualizing high-dimensional datasets. It maps multi-dimensional data points into a lower-dimensional space (typically 2D or 3D) in a way that preserves the local structure of the data, meaning similar points in the high-dimensional space remain close in the low-dimensional map.

Unlike linear methods like PCA, t-SNE can capture complex, non-linear relationships, making it highly effective at revealing hidden clusters and patterns in data.

Why Use t-SNE?

• Reveals Clusters: Excellent at making distinct clusters of data points visible in lower dimensions, even when those clusters are intertwined in the original high-dimensional space.
• Non-linear Relationships: Can uncover complex, non-linear structures that linear methods (like PCA) might miss.
• Visualization Power: Produces visually appealing and intuitive maps that are easy for humans to interpret, helping to understand complex data structures at a glance.
• Exploratory Data Analysis: A powerful tool for exploratory data analysis, pattern recognition, and validating clustering results.

Real-World Example: Handwritten Digits

This is a t-SNE visualization of hundreds of handwritten digits. Each image is 64-dimensional data, reduced to 2D. The algorithm groups similar-looking numbers together. Hover over a point to see the original image!