We present two approaches that significantly reduce the computational cost of applying the EM algorithm to databases with a large number of cases, including ...
We have considered two approaches for accelerating the EM algorithm for large databases. Both approaches, the incremental EM and lazy EM, are based on ...
We present two approaches that significantly reduce the computa-tional cost of applying the EM algorithm to databases with a large number of cases, including ...
Jan 1, 2001 · We present two approaches that significantly reduce the computational cost of applying the EM algorithm. Both approaches are based on partial E- ...
We demonstrate that both methods can significantly reduce computational costs through their application to high-dimensional real-world and synthetic mixture ...
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Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions ...
We derive an accelerated EM algorithm that strictly increases in each step a lower bound on the data log-likelihood —independent of the chosen partitioning— ...
Bo Thiesson, Christopher Meek, David Heckerman: Accelerating EM for Large Databases. Mach. Learn. 45(3): 279-299 (2001). manage site settings.
May 11, 2017 · The reformulation of the orthogonalizing EM algorithm leads to a reduction in computational complexity for least-square problems and penalized ...
We demonstrate that the scalable EM algorithm (SEM) indeed preserves clustering fidelity and that it outperforms traditional methods addressing large databases ...
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