...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neurocomputing >Kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters
【24h】

Kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters

机译：基于内核的MinMax聚类方法，带有度量化和自动调整超参数的内核化

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

This paper proposes kernel-based MinMax clustering methods with kernelization of the metric and autotuning hyper-parameters which learn the variable weights and adjust the cluster weights automatically. We develop the new objective functions that are obtained from the proposed algorithms to achieve the desirable partition by minimizing the dissimilarity measures with kernelization of the metric. Correspondingly, two additional steps are introduced to k-means algorithms, so that, not only the performance is improved, but also the efficiency remains. More specifically, the proposed algorithms learn two types of weights at each iteration where variable weights identify relevant variables and cluster weights to confine the occurrence of the large variance cluster. Finally, the experiments on ten UCI benchmark datasets corroborate the superiority of the proposed algorithms. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文提出了一种基于核的MinMax聚类方法，其中对度量进行核化并自动调整超参数，以学习可变权重并自动调整聚类权重。我们开发了从提出的算法中获得的新目标函数，以通过将度量的核化最小化相异性度量来实现所需的分区。相应地，将两个额外的步骤引入到k均值算法中，从而不仅提高了性能，而且保持了效率。更具体地，所提出的算法在每次迭代中学习两种类型的权重，其中，可变权重标识相关变量，并且利用聚类权重来限制大方差聚类的出现。最后，在十个UCI基准数据集上的实验证实了所提出算法的优越性。 （C）2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2019年第24期|173-184|共12页
  • 作者

    Liu Junyan; Guo Yongan; Li Dapeng; Wang Zefeng; Xu Youyun;

  • 作者单位

    Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China;

    Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China;

    Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China|Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China;

    Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China;

    Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Kernel clustering; Kernelization of the metric; Auto-tuning hyper-parameters; MinMax optimization;

    机译：内核聚类;公制的内核;自动调整超参数;minmax优化;

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