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Found 3 papers, 1 papers with code
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities
Manifold learning approaches seek the intrinsic, low-dimensional data structure within a high-dimensional space.
Geometry- and Accuracy-Preserving Random Forest Proximities
Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.
Supervised Visualization for Data Exploration
We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structures in data, while emphasizing important variables in the low-dimensional embedding.
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