# T-SNE ### Stochastic Neighbor Embedding {% hint style="success" %} T-SNE is a machine learning algorithm for data visualization (usually), which is based on a nonlinear dimensionality reduction technique. {% endhint %} The basic idea of t-SNE is to reduce dimensional space keeping relative pairwise distance between points. In other words, the algorithm maps multi-dimensional data to two or more dimensions, where points which were initially far from each other are also located far away, and close points are also converted to close ones. > It can be said that t-SNE looking for a new data representation where the neighborhood relations are preserved. ### References {% embed url="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ai.nuhil.net/data-science/t-sne.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.