Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields

TL;DR

This paper introduces Contrastive Feature Induction (CFI), a fast and efficient method for structure learning in CRFs that selectively evaluates features based on signals and errors, reducing computation time while maintaining accuracy.

Contribution

The paper proposes CFI, a novel feature evaluation algorithm that approximates gradient-based methods by focusing on high-signal and high-error features, improving efficiency in high-dimensional CRF structure learning.

Findings

CFI achieves competitive speed and accuracy compared to full optimization and Grafting.

CFI reduces evaluation costs by focusing on relevant features involving high signals and errors.

Experiments demonstrate CFI's effectiveness on synthetic and real datasets.

Abstract

Structure learning of Conditional Random Fields (CRFs) can be cast into an L1-regularized optimization problem. To avoid optimizing over a fully linked model, gain-based or gradient-based feature selection methods start from an empty model and incrementally add top ranked features to it. However, for high-dimensional problems like statistical relational learning, training time of these incremental methods can be dominated by the cost of evaluating the gain or gradient of a large collection of candidate features. In this study we propose a fast feature evaluation algorithm called Contrastive Feature Induction (CFI), which only evaluates a subset of features that involve both variables with high signals (deviation from mean) and variables with high errors (residue). We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods. Experiments on synthetic and real data sets show competitive learning speed and accuracy of CFI on pairwise CRFs, compared to state-of-the-art structure learning methods such as full optimization over all features, and Grafting.