Hi All, not sure if this section is the right section to post a question about the Knime Labs Statistics Node package I’m trying out the t-SNE package, but i’m having some troubles wrapping my head around how to use it in a production setting, where i want to apply the transformation on fresh incoming data [image] t-SNE (L. Jonsson) t-SNE is a manifold learning technique, which learns low dimensional embeddings for high dimensional data. It is most often used for visualization purposes bec… [image] Feature Suggestion -> t-distributed stochastic neighbor embedding (T-SNE) KNIME Analytics Platform T-SNE is rapidly becoming a key algorithm for dimension reduction in many fields including ours, rivaling the use of PCA. Unfortunately, T-SNE in Knime requires either the use of R/Python or a very alpha community node that doesn’t match r/python results. It would be helpful to have a built-in T-SNE node. [image] t-SNE implementation in Knime KNIME Extensions Hello, Did anyone successfully created or attempted to use t-SNE in Knime? Is t-SNE in Knime's future plans? Thanks! I was actually looking for a similar approach as the PCA Compute / Apply setup; where you can compute the transformation, and then export PMML and import it into the Production pipeline for the Apply step [image] is a similar approach possible? or how to go about applying the transformation on new data? – e.g. outside of a train-test workbench Thx a bunch! Herman

t-SNE - how to use in production pipeline?

mlauber71 November 29, 2022, 10:47am 6

As an addition these articles from the KNIME low-code blog on Medium:

Seven Techniques for Data Dimensionality Reduction

Three More Techniques for Data Dimensionality Reduction in ML

3 Likes

How to evaluate the performance of a dimensionality reduction algorithm

Outcome of a Neural Network is too good. Where did I go wrong