<ul><li>Fairness of decision-making algorithms is an increasingly important issue.</li><li>New efficient method for fair spectral clustering (Fair SC) presented by casting the Fair SC problem within the difference of convex functions framework.</li><li>Introduces a novel variable augmentation strategy and employs an alternating direction method of multipliers type of algorithm adapted to DC problems.</li><li>Numerical experiments demonstrate the effectiveness of the approach on synthetic and real-world benchmarks, showing significant speedups in computation time over prior art.</li></ul>

Accelerating Spectral Clustering under Fairness Constraints

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