Spectral partitioning speeds up convergence in finite Markov chains, paper details algorithm
michaelchchoi · x · 2026-08-25
This paper develops spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. It selects partitions by rounding the bottom nonconstant eigenfunctions of the transition matrix using weighted k-means. The authors derive exact trace and normalized-cut representations, showing the objective equals the Pearson χ² mutual information.
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