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This paper introduces a recursion formula for efficiently computing the stochastic complexity of vectors with cluster structure using the Normalized Maximum Likelihood model, reducing time complexity from polynomial to linear.
This paper presents exact dimensionality reductions using Schur complement and Sylvester's determinant identity to reduce computational complexity from O(N^3) to O(k^3+N^2k) per step for non-smooth NML estimation, achieving over 14,000x speedup while maintaining numerical precision.