...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Structured Dimensionality Reduction for Additive Model Regression
【24h】

Structured Dimensionality Reduction for Additive Model Regression

机译：结构维数减少以实现附加模型回归

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models are however not adapted to problems involving a large number (e.g., hundreds) of input variables, as they are prone to overfitting in addition to losing interpretability. In this paper, we introduce a novel framework for applying additive models to a large number of input variables. The key idea is to reduce the task dimensionality by deriving a small number of new covariates obtained by linear combinations of the inputs, where the linear weights are estimated with regard to the regression problem at hand. The weights are moreover constrained to prevent overfitting and facilitate the interpretation of the derived covariates. We establish identifiability of the proposed model under mild assumptions and present an efficient approximate learning algorithm. Experiments on synthetic and real-world data demonstrate that our approach compares favorably to baseline methods in terms of accuracy, while resulting in models of lower complexity and yielding practical insights into high-dimensional real-world regression tasks. Our framework broadens the applicability of additive models to high-dimensional problems while maintaining their interpretability and potential to provide practical insights.
机译：加法模型是将响应变量建模为输入变量的单变量传递函数之和的回归方法。加性模型的主要优点是它们在许多实际任务中的准确性和可解释性。然而，加性模型不适用于涉及大量（例如数百个）输入变量的问题，因为它们除了失去可解释性之外还易于过度拟合。在本文中，我们介绍了一个将加性模型应用于大量输入变量的新颖框架。关键思想是通过导出输入线性组合而获得的少量新协变量来减少任务维数，其中线性权重是针对当前回归问题估算的。此外，权重受到约束以防止过度拟合并有助于对派生协变量的解释。我们在温和的假设下建立了所提出模型的可识别性，并提出了一种有效的近似学习算法。对合成数据和真实世界数据进行的实验表明，我们的方法在准确性方面优于基线方法，同时可以降低模型的复杂度，并提供对高维真实世界回归任务的实用见解。我们的框架将加性模型的应用范围扩展到高维问题，同时保持其可解释性和提供实际见解的潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号