Summary of Enhancing Supervised Visualization Through Autoencoder and Random Forest Proximities For Out-of-sample Extension, by Shuang Ni et al.
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
by Shuang Ni, Adrien Aumon, Guy Wolf, Kevin R. Moon, Jake S. Rhodes
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an out-of-sample extension method for random forest-based supervised dimensionality reduction, RF-PHATE. The approach combines information learned from the random forest model with autoencoders’ function-learning capabilities. The authors identify that networks reconstructing random forest proximities are more robust for the embedding extension problem. They also introduce proximity-based prototypes, which reduce training time by 40% without compromising quality. The method is semi-supervised, requiring only 10% of the training data to achieve consistent results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to use machine learning to better understand how different pieces of information are connected. It takes a common approach and makes it work with new, unseen data. To do this, it combines two techniques: one that uses many decision trees (random forests) and another that tries to recreate the original data points from some hidden, lower-dimensional representation (autoencoders). The authors test different ways of combining these approaches and find that a specific combination works well. They also develop a new way to create “prototypes” that helps the method work more efficiently. |
Keywords
» Artificial intelligence » Dimensionality reduction » Embedding » Machine learning » Random forest » Semi supervised » Supervised
