<!DOCTYPE html><html><head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> </head> <body text="#000000" bgcolor="#FFFFFF"> A new article is available in IPOL: <a class="moz-txt-link-freetext" href="https://www.ipol.im/pub/art/2024/528/">https://www.ipol.im/pub/art/2024/528/</a> Sangwon Jung, Tristan Dagobert, Jean-Michel Morel, and Gabriele Facciolo, A Review of t-SNE, Image Processing On Line, 14 (2024), pp. 250–270. <a class="moz-txt-link-freetext" href="https://doi.org/10.5201/ipol.2024.528">https://doi.org/10.5201/ipol.2024.528</a> Abstract <p style="text-align: justify; color: rgb(0, 0, 0); font-family: Arial, Helvetica, Tahoma, sans-serif; font-size: 14.4px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">High dimensional data is difficult to visualize. T-Distributed Stochastic Neighbor Embedding (t-SNE) is a popular technique for dimensionality reduction enabling a planar visualization of a dataset preserving as much as possible its metric. This paper explores the theoretical background of t-SNE and its accelerated version. A comparison of the performance of t-SNE on various datasets with different dimensions is also performed. </body> </html>