Adaptive Supervised Learning on Data Streams in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint.
Adaptive Supervised Learning on Data Streams in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint.
复制标题
具有数据稀疏约束的再生核希尔伯特空间中数据流的自适应监督学习。
作者：
Wang,Haodong;Li,Quefeng;Liu,Yufeng
如今，许多学科的数据以前所未有的速度和规模生成。流数据分析领域的出现是各个领域新数据收集和存储技术的结果，例如空气污染监测、交通拥堵检测、疾病监测和推荐系统。在本文中，我们考虑再生核希尔伯特空间中数据流的模型估计问题。我们提出了一种具有数据稀疏约束的自适应监督学习方法，该方法使用有限的存储空间并可以处理非平稳模型。我们通过对自行车共享数据集的模拟和分析来证明所提出方法的竞争性能。
Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset.