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Approximation Analysis of Gradient Descent Algorithm for Bipartite Ranking

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  • Hong Chen
  • Fangchao He
  • Zhibin Pan

Abstract

We introduce a gradient descent algorithm for bipartite ranking with general convex losses. The implementation of this algorithm is simple, and its generalization performance is investigated. Explicit learning rates are presented in terms of the suitable choices of the regularization parameter and the step size. The result fills the theoretical gap in learning rates for ranking problem with general convex losses.

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  • Hong Chen & Fangchao He & Zhibin Pan, 2012. "Approximation Analysis of Gradient Descent Algorithm for Bipartite Ranking," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-13, July.
    • Handle: RePEc:hin:jnljam:189753
    DOI: 10.1155/2012/189753
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    Cited by:

    1. Wenjie Huang & Xun Zhang, 2021. "Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization," SN Operations Research Forum, Springer, vol. 2(2), pages 1-28, June.

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