Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
📰 ArXiv cs.AI
Learn to reduce data dimensions while preserving predictive power using supervised distributional reduction via optimal transport and dependence maximization
Action Steps
- Apply optimal transport to minimize distributional distance between original and reduced data
- Use dependence maximization to retain target-relevant structure in reduced representations
- Configure reduction algorithms to balance compression and predictive fidelity
- Test the approach on benchmark datasets to evaluate its effectiveness
- Compare results with existing dimensionality reduction techniques to assess performance gains
Who Needs to Know This
Data scientists and ML engineers can apply this technique to improve model performance and reduce data complexity, while researchers can explore its applications in various domains
Key Insight
💡 Supervised distributional reduction can effectively balance data compression and predictive fidelity by leveraging optimal transport and dependence maximization
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🚀 Reduce data dimensions without losing predictive power! Supervised distributional reduction via optimal transport and dependence maximization 🤯
Key Takeaways
Learn to reduce data dimensions while preserving predictive power using supervised distributional reduction via optimal transport and dependence maximization
Full Article
Title: Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
Abstract:
arXiv:2605.27619v1 Announce Type: cross Abstract: Learning representations that capture both intrinsic data geometry and target-relevant structure remains a fundamental challenge, particularly in settings where data reduction must balance compression with predictive fidelity. While distributional reduction-encompassing joint clustering and dimensionality reduction-offers a principled way to summarize data, its supervised variants remain relatively under-explored, despite the importance of retain
Abstract:
arXiv:2605.27619v1 Announce Type: cross Abstract: Learning representations that capture both intrinsic data geometry and target-relevant structure remains a fundamental challenge, particularly in settings where data reduction must balance compression with predictive fidelity. While distributional reduction-encompassing joint clustering and dimensionality reduction-offers a principled way to summarize data, its supervised variants remain relatively under-explored, despite the importance of retain
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