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.
Therefore, the only linear combination of the gradients ∇xψij(¯x)that equals zero is the trivial one, which proves thatC(¯x)is linearly independent
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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.