t-SNE Insights

What is t-SNE?

t-SNE (t-distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique primarily used for visualizing high-dimensional data in lower dimensions (typically 2 or 3 dimensions). It works by modeling each high-dimensional object by a low-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points. It is commonly used to explore and visualize clusters or patterns in complex datasets, such as image data, gene expression data, and text documents.

Find organizations using t-SNE on Sumble →

What other technologies are related to t-SNE?

t-SNE Competitor Technologies

UMAP

UMAP is another dimensionality reduction technique similar to t-SNE, often used for visualization and pre-processing for machine learning tasks. It is frequently compared to t-SNE and offers advantages in terms of speed and global structure preservation.

mentioned alongside t-SNE in 41% (68) of relevant job posts

PCA

PCA (Principal Component Analysis) is a dimensionality reduction technique that, like t-SNE, can be used for visualizing high-dimensional data in lower dimensions. While it has different strengths and weaknesses compared to t-SNE (e.g., PCA is linear, t-SNE is non-linear), they both serve a similar purpose.

mentioned alongside t-SNE in 3% (95) of relevant job posts

t-SNE Complementary Technologies

R

R is a programming language and environment commonly used for statistical computing and graphics. t-SNE is implemented in R, and R can be used for pre- and post-processing data for t-SNE.

mentioned alongside t-SNE in 0% (94) of relevant job posts

TensorFlow

TensorFlow is a deep learning framework that can be used to implement t-SNE or as a component in workflows that use t-SNE for visualization or feature extraction. Can also be used as the back end to train models whose learned representations are visualized with t-SNE.

mentioned alongside t-SNE in 0% (52) of relevant job posts

PyTorch

PyTorch is a deep learning framework that can be used to implement t-SNE or as a component in workflows that use t-SNE for visualization or feature extraction. Can also be used as the back end to train models whose learned representations are visualized with t-SNE.

mentioned alongside t-SNE in 0% (51) of relevant job posts