Primal sparse Max-margin Markov networks

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Primal sparse Max-margin Markov networks
Max-margin Markov networks (M3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M3 N enjoys dual sparsity. However, the existing M3 N formulation does not enjoy primal sparsity, which is a desirable property for selecting significant features and reducing the risk of over-fitting. In this paper, we present an 1-norm regularized max-margin Markov network ( 1-M3 N), which enjoys dual and primal sparsity simultaneously. To learn an 1-M3 N, we present three methods including projected sub-gradient, cutting-plane, and a novel EM-style algorithm, which is based on an equivalence between 1-M3 N and an adaptive M3 N. We perform extensive empirical studies on both synthetic and real data sets. Our experimental results show that: (1) 1-M3 N can effectively select significant features; (2) 1-M3 N can perform as well as the pseudo-primal sparse Laplace M3 N in prediction accuracy, while consistently outperforms other competing method...
Jun Zhu, Eric P. Xing, Bo Zhang
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where KDD
Authors Jun Zhu, Eric P. Xing, Bo Zhang
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