> For the complete documentation index, see [llms.txt](https://edu.abi.am/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://edu.abi.am/statistics-theory/dimentionality-reduction/t-sne-t-distributed-stochastic-neighbor-embedding.md).

# 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="<https://www.youtube.com/watch?v=NEaUSP4YerM>" %}