Summary of Gradient Boosting Mapping For Dimensionality Reduction and Feature Extraction, by Anri Patron et al.
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
by Anri Patron, Ayush Prasad, Hoang Phuc Hau Luu, Kai Puolamäki
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers tackle the fundamental problem of finding optimal features or distance measures in supervised learning. They propose Gradient Boosting Mapping (GBMAP), a method that uses weak learners to define an embedding that can improve model understandability, reduce overfitting, and detect distribution drift. GBMAP is shown to provide better features for simple linear models, making them competitive with state-of-the-art regressors and classifiers. The paper also explores using the embedding to find a principled distance measure between points, which ignores irrelevant directions and reliably detects out-of-distribution data points. GBMAP is fast and efficient, working in seconds on large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us make machines smarter by finding better ways to understand patterns in data. The team created a new method called Gradient Boosting Mapping (GBMAP) that can help simplify complex models and improve their performance. By using this method, simple models can be just as good as more complicated ones. The researchers also showed how GBMAP can detect when new data doesn’t fit with what the model has learned before. This is important because it helps machines avoid making mistakes when they encounter unfamiliar information. |
Keywords
» Artificial intelligence » Boosting » Embedding » Overfitting » Supervised
