Recently, non-linear dimension-reduction and visualization algorithms, most notably t-Distributed Stochastic Neighbor Embedding (t-SNE) and uniform manifold approximation and projection (UMAP), have been widely applied to various research areas such as image processing, text mining, and genomics. This Add-in provides access to both t-SNE and UMAP R packages. It offers a user-friendly interface enabling data table navigation, data quality control, sparsity handling, intuitive parameterization, and interactive results interpretation.
Here is a screenshot of the interface with MNIST data loaded. Under Model Specifications, I selected the label column as Label and all the pixels as predictors. I chose both t-SNE and UMAP as the algorithms.
Another screenshot of the results of both t-SNE (top) and UMAP (bottom).
This add-in also supports some basic quality control options, including missing value checking, distribution, and sparsity calculation. You can find these options under Quality Control Options on the interface.
Updates: JMP R interface on Mac has versioning issues. Please downgrade your R to version <=3.3.3, and use t-sne only if you are a Mac user.
v.1.2 <3/14/2019> Fixed an issue in Rtsne package where a large number of columns causing stack overflow problem. v.1.1 <3/8/2019> Fixed a bug that could potentially produce “issues found in R, memory exhausted?” error message. Added a submenu for the MNIST example dataset.