• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Distributed randomized singular value decomposition using count sketch
【24h】

Distributed randomized singular value decomposition using count sketch

机译：使用计数草图的分布式随机奇异值分解

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

Compared with other recommendation algorithms, Matrix decomposition is frequently used in the current recommendation system. It can not only lead to better results, but also can fully take the influence of various factors into account, which explains its good scalability. Matrix decomposition includ-es SVD(Singular Value Decomposition), non-negative matrix decomposition, Latent Factor Model and some other traditional matrix decomposition techniques is designed to approximate a high-dimensional matrix with low-dimensional. As a perfect technique in recommendation system, SVD is traditionally expert at dense matrix decomposition. However, real rating matrix are sparse, and have high time complexity of SVD, if the matrix size increases rapidly, the efficiency must become unacceptable. The combination of random algorithm and matrix decomposition turns traditional matrix decomposition into random matrix decomposition technique under distributed system environment. The random singular value decomposition technique illustrated in the following content can be at the expense of little accuracy under the premise of greatly improving the efficiency of the calculation.
机译：与其他推荐算法相比，矩阵分解在当前推荐系统中经常使用。它不仅可以带来更好的结果，而且可以充分考虑各种因素的影响，从而说明其良好的可伸缩性。设计了包括SVD（奇异值分解），非负矩阵分解，潜在因子模型和其他一些传统矩阵分解技术的矩阵分解方法，以逼近具有低维的高维矩阵。作为推荐系统中的一项完美技术，SVD传统上是密集矩阵分解方面的专家。但是，实际评级矩阵是稀疏的，并且具有较高的SVD时间复杂度，如果矩阵大小迅速增加，效率必将变得无法接受。随机算法与矩阵分解的结合将传统的矩阵分解转变为分布式系统环境下的随机矩阵分解技术。在大幅度提高计算效率的前提下，以下内容所示的随机奇异值分解技术可能会以精度较低为代价。

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