KNIME SMILE Machine Learning Integration version 4.2.0.v202004222203 by KNIME AG, Zurich, Switzerland
t-SNE is a manifold learning technique that learns low-dimensional embeddings for high-dimensional data. It is most often used for visualization purposes because it exploits the local relationships between data points and can hence capture non-linear structures in the data. Unlike other dimension reduction techniques like PCA, a learned t-SNE model can't be applied to new data. The t-SNE algorithm can be roughly summarized as two steps:
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.