# t-SNE (t-Distributed Stochastic Neighbor Embedding) **Overview:** * **t-SNE** is a non-linear dimensionality reduction technique primarily used for visualization. * It converts high-dimensional Euclidean distances into conditional probabilities that represent similarities. * The algorithm minimizes the Kullback-Leibler divergence between these probability distributions in the high-dimensional and low-dimensional space. **Key Characteristics:** * Effective at creating a visual representation of complex data, revealing clusters and patterns. * Sensitive to parameters such as perplexity and learning rate. * Computationally intensive, especially for large datasets. **Applications:** * Visualizing high-dimensional data such as images, text, and gene expression data. * Exploring and understanding the structure of the data. {% 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://edu.abi.am/statistics-theory/dimentionality-reduction/t-sne-t-distributed-stochastic-neighbor-embedding.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.