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|Title:||Accelerating Newton optimization for log-linear models through feature redundancy|
|Citation:||Proceedings of the Sixth International Conference on Data Mining, Hong Kong, China, 18-22 December 2006, 1-10|
|Abstract:||Log-linear models are widely used for labeling feature vectors and graphical models, typically to estimate robust conditional distributions in presence of a large number of potentially redundant features. Limited-memory quasi-Newton methods like LBFGS or BLMVM are optimization workhorses for such applications, and most of the training time is spent computing the objective and gradient for the optimizer. We propose a simple technique to speed up the training optimization by clustering features dynamically, and interleaving the standard optimizer with another, coarse-grained, faster optimizer that uses far fewer variables. Experiments with logistic regression training for text classification and conditional random field (CRF) training for information extraction show promising speed-ups between 2× and 9× without any systematic or significant degradation in the quality of the estimated models.|
|Appears in Collections:||Proceedings papers|
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