• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Maximally Split and Relaxed ADMM for Regularized Extreme Learning Machines
【24h】

A Maximally Split and Relaxed ADMM for Regularized Extreme Learning Machines

机译：对于正规化的极端学习机器，最大分裂和放松的ADMM

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

One of the salient features of the extreme learning machine (ELM) is its fast learning speed. However, in a big data environment, the ELM still suffers from an overly heavy computational load due to the high dimensionality and the large amount of data. Using the alternating direction method of multipliers (ADMM), a convex model fitting problem can be split into a set of concurrently executable subproblems, each with just a subset of model coefficients. By maximally splitting across the coefficients and incorporating a novel relaxation technique, a maximally split and relaxed ADMM (MS-RADMM), along with a scalarwise implementation, is developed for the regularized ELM (RELM). The convergence conditions and the convergence rate of the MS-RADMM are established, which exhibits linear convergence with a smaller convergence ratio than the unrelaxed maximally split ADMM. The optimal parameter values of the MS-RADMM are obtained and a fast parameter selection scheme is provided. Experiments on ten benchmark classification data sets are conducted, the results of which demonstrate the fast convergence and parallelism of the MS-RADMM. Complexity comparisons with the matrix-inversion-based method in terms of the numbers of multiplication and addition operations, the computation time and the number of memory cells are provided for performance evaluation of the MS-RADMM.
机译：极端学习机（ELM）的突出特征之一是其快速学习速度。然而，在大数据环境中，ELM仍然存在由于高维度和大量数据而导致的过高的计算负荷。使用乘法器（ADMM）的交替方向方法，可以将凸模型拟合问题分成一组同步可执行的子问题，每个子项本都只有模型系数的子集。通过最大地分裂系数并结合新颖的松弛技术，为正则化的ELM（relm）开发了最大分裂和弛豫的ADMM（MS-RADMM）以及乘标量实现。建立了MS-RADMM的收敛条件和收敛速率，其表现出具有较小收敛比的线性会聚，比未加速的最大分割ADMM更小。获得MS-RADMM的最佳参数值，并提供快速参数选择方案。进行了十个基准分类数据集的实验，结果表明了MS-Radmm的快速收敛性和并行性。在乘法和添加操作的数量方面，对基于矩阵反演的方法的复杂性比较，提供了用于MS-RADMM的性能评估的计算时间和存储器的数量。

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