Open mglowacki100 opened 2 years ago
In principle you can build a custom metric (say, similar to jaccard) over the multi-labels, and use that are the target_metric
parameters for a surpervised UMAP. You could also try using ParametricUMAP in it's semi-supervised mode and use an appropriate NN architecture for multi-label classification for the classifier portion.
Is there a natural way to extend supervised UMAP to multi-label classification? Of course there is naive approach to transform multi-label into multiclass, but I wonder if there are other approaches possible/makes sense e.g. :
Maybe there are other approaches?