Publication date: 04/21/2023
Multivariate Embedding
Map High Dimensional Data to a Low Dimensional Space
The Multivariate Embedding platform is available only in JMP Pro.
The Multivariate Embedding platform enables you to map data from very high-dimensional spaces to a low-dimensional space. Many times, you want to map the data to either two or three dimensions so that the low-dimensional space can be easily visualized. In the Multivariate Embedding platform, you can use either the Uniform Manifold Approximation and Projection (UMAP) method or the t-Distributed Stochastic Neighbor Embedding (t-SNE) method. Both methods attempt to fill the low-dimensional space in such a way that clusters of near neighbors can be more easily identified.
Figure 11.1 Example of Multivariate EmbeddingÂ
Contents
Overview of the Multivariate Embedding Platform
Example of Multivariate Embedding
Launch the Multivariate Embedding Platform
The Multivariate Embedding Report
Multivariate Embedding Platform Options
Additional Example of Multivariate Embedding
Statistical Details for the Multivariate Embedding Platform
Statistical Details for the t-SNE Method
Statistical Details for the Gradient Descent Algorithm
Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).