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Are there any visualization libraries that visualize embeddings in a vector space?

• f_devd 2y ago
  UMAP: https://umap-learn.readthedocs.io/en/latest/ https://umap-learn.readthedocs.io/en/latest/ scikit-learn also has options: https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py https://scikit-learn.org/stable/auto_examples/manifold/plot_...
• sk11001 2y ago
  There's attempts but you can only do so much in hundreds/thousands of dimensions. Most of the time the visualization doesn't really provide anything meaningful.
• beejiu 2y ago
  My instinct would be a principal component analysis (which someone has demonstrated here: https://www.youtube.com/watch?app=desktop&v=brt88wwoZtI https://www.youtube.com/watch?app=desktop&v=brt88wwoZtI). Not sure it would tell you much though, but it looks nice.
• OutOfHere 2y ago
  If you need them visualized, you're already on the wrong track.
• lmcinnes 2y ago
  Assuming you have a dimension-reduction or manifold learning tool of choice (UMAP,PacMAP,t-SNE,PyMDE,etc.) then DataMapPlot (https://datamapplot.readthedocs.io/en/latest/ https://datamapplot.readthedocs.io/en/latest/) is a library specifically designed to make visualizations of the outputs of your dimension reduction.