A new ADMM-based difference-of-convex algorithm solves fair spectral clustering without n×n eigendecomposition, reducing runtime 4-8x versus prior state-of-the-art at comparable fairness and clustering cost.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Accelerating Spectral Clustering under Fairness Constraints
A new ADMM-based difference-of-convex algorithm solves fair spectral clustering without n×n eigendecomposition, reducing runtime 4-8x versus prior state-of-the-art at comparable fairness and clustering cost.