Cloud K-SVD: Computing data-adaptive representations in the cloud
Cloud K-SVD: Computing data-adaptive representations in the cloud
复制标题
Cloud K-SVD：在云中计算数据自适应表示
DOI：
10.1109/allerton.2013.6736701
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
W. Bajwa
中科院分区：
文献类型：
--
作者：
Haroon Raja;W. Bajwa
本文研究了分布式大数据的数据自适应表示问题。假设一些地理上分布的、相互连接的站点具有大量的本地数据，并且它们有兴趣协作地学习这些数据的低维几何结构。与以前的一些关于子空间表示的工作相比，本文重点研究子空间的并集的几何结构。具体来说，它提出了一种分布式算法，称为云K-SVD，用于学习UoS结构的分布式数据的兴趣。Cloud K-SVD实现了协作数据自适应表示的目标，而无需在不同站点之间传输单个数据样本。本文还提供了云K-SVD的部分分析，从局部数据的属性和互连拓扑结构的角度，深入了解其收敛特性和偏离集中式解决方案。最后，数值分析了云K-SVD的有效性。
This paper studies the problem of data-adaptive representations for big, distributed data. It is assumed that a number of geographically-distributed, interconnected sites have massive local data and they are interested in collaboratively learning a low-dimensional geometric structure underlying these data. In contrast to some of the previous works on subspace representations, this paper focuses on the geometric structure of a union of subspaces (UoS). Specifically, it proposes a distributed algorithm, termed as cloud K-SVD, for learning a UoS structure underlying distributed data of interest. Cloud K-SVD accomplishes the goal of collaborative data-adaptive representations without requiring communication of individual data samples between different sites. The paper also provides a partial analysis of cloud K-SVD that gives insights into its convergence properties and deviations from a centralized solution in terms of properties of local data and topology of interconnections. Finally, it numerically illustrates the efficacy of cloud K-SVD.