Marginal Structured SVM with Hidden Variables

TL;DR

This paper introduces the marginal structured SVM (MSSVM), a new method for structured prediction with hidden variables that improves accuracy and convergence speed over previous models by better handling uncertainty.

Contribution

The paper presents MSSVM, a novel approach that accounts for hidden variable uncertainty, offers a unified framework, and demonstrates superior performance and faster convergence.

Findings

MSSVM outperforms LSSVM and HCRFs on various datasets.

MSSVM converges faster due to a smoother objective function.

Unified framework includes multiple existing methods.

Abstract

In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, especially when that uncertainty is large. Our method also results in a smoother objective function, making gradient-based optimization of MSSVMs converge significantly faster than for LSSVMs. We also show that our method consistently outperforms hidden conditional random fields (HCRFs; Quattoni et al. (2007)) on both simulated and real-world datasets. Furthermore, we propose a unified framework that includes both our and several other existing methods as special cases, and provides insights into the comparison of different models in practice.