Marginal Structured SVM with Hidden Variables
This addresses structured prediction problems with hidden variables, offering improved performance and optimization efficiency over existing methods.
The authors tackled structured prediction with hidden variables by proposing the marginal structured SVM (MSSVM), which accounts for hidden variable uncertainty and outperforms latent structured SVM and hidden conditional random fields, with faster convergence and consistent gains on simulated and real-world datasets.
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.