Dimensionality reduction is a technique used to simplify complex datasets by reducing the number of variables or features while preserving the essential patterns and relationships within the data. One popular method for dimensionality reduction is t-SNE, which stands for t-Distributed Stochastic Neighbor Embedding. t-SNE is particularly effective for visualizing high-dimensional data in lower dimensions, such as two or three dimensions, making it easier to identify patterns, clusters, or anomalies.

t-SNE works by converting the distances between data points in high-dimensional space into probabilities that reflect the likelihood of one point being a neighbor of another. It then maps these probabilities to a lower-dimensional space while attempting to maintain the local structure of the data. This process helps to uncover hidden structures and relationships that might not be apparent in the original high-dimensional space.

Because of its ability to preserve local similarities and reveal intricate structures, t-SNE is widely used in exploratory data analysis and visualization, especially in fields like machine learning and bioinformatics. However, it’s worth noting that t-SNE can be computationally intensive and might not always preserve global relationships between data points.