Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clustering
Published:Dec 10, 2025 16:25
•1 min read
•ArXiv
Analysis
This article, sourced from ArXiv, focuses on the intersection of fairness and spectral clustering, a common unsupervised machine learning technique. The title suggests an investigation into how to make spectral clustering algorithms more equitable by considering fairness constraints within the neighborhood graph construction process. The research likely explores methods to mitigate bias and ensure fair representation across different groups within the clustered data. The use of 'neighborhood graphs' indicates a focus on local relationships and potentially graph-based techniques to achieve fairness.
Key Takeaways
- •Focuses on fairness in spectral clustering.
- •Uses neighborhood graphs for fair clustering.
- •Aims to mitigate bias and ensure equitable representation.
Reference
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